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2020 – today
- 2024
[j106]Khaled Saab
, Siyi Tang, Mohamed Taha, Christopher Lee-Messer, Christopher Ré, Daniel L. Rubin:
Towards trustworthy seizure onset detection using workflow notes. npj Digit. Medicine 7(1) (2024)- [j105]Sarthak Pati, Sourav Kumar, Amokh Varma, Brandon Edwards, Charles Lu, Liangqiong Qu, Justin J. Wang, Anantharaman Lakshminarayanan, Shih-han Wang, Micah J. Sheller, Ken Chang, Praveer Singh, Daniel L. Rubin, Jayashree Kalpathy-Cramer, Spyridon Bakas:
Privacy preservation for federated learning in health care. Patterns 5(7): 100974 (2024) - [j104]Zexuan Ji, Xiao Ma, Theodore Leng, Daniel L. Rubin, Qiang Chen:
Mirrored X-Net: Joint classification and contrastive learning for weakly supervised GA segmentation in SD-OCT. Pattern Recognit. 153: 110507 (2024) - [j103]Nandita Bhaskhar, Daniel L. Rubin, Christopher Lee-Messer:
An Explainable and Actionable Mistrust Scoring Framework for Model Monitoring. IEEE Trans. Artif. Intell. 5(4): 1473-1485 (2024) - [c83]Sharut Gupta, Ken Chang, Liangqiong Qu, Aakanksha Rana, Syed Rakin Ahmed, Mehak Aggarwal, Nishanth Thumbavanam Arun, Ashwin Vaswani, Shruti Raghavan, Vibha Agarwal, Mishka Gidwani, Katharina Hoebel, Jay B. Patel, Charles Lu, Christopher P. Bridge, Daniel L. Rubin, Jayashree Kalpathy-Cramer, Praveer Singh:
Addressing Catastrophic Forgetting by Modulating Global Batch Normalization Statistics for Medical Domain Expansion. AIPAD/PILM@MICCAI 2024: 57-72 - 2023
- [j102]Okyaz Eminaga, Mahmoud Abbas, Jeanne Shen, Mark A. Laurie, James D. Brooks, Joseph C. Liao, Daniel L. Rubin:
PlexusNet: A neural network architectural concept for medical image classification. Comput. Biol. Medicine 154: 106594 (2023) - [j101]Nandita Bhaskhar, Wui Ip, Jonathan H. Chen, Daniel L. Rubin:
Clinical outcome prediction using observational supervision with electronic health records and audit logs. J. Biomed. Informatics 147: 104522 (2023) - [j100]Siyi Tang, Amara Tariq, Jared A. Dunnmon, Umesh Sharma, Praneetha Elugunti, Daniel L. Rubin, Bhavik N. Patel, Imon Banerjee:
Predicting 30-Day All-Cause Hospital Readmission Using Multimodal Spatiotemporal Graph Neural Networks. IEEE J. Biomed. Health Informatics 27(4): 2071-2082 (2023) - [j99]Rui Yan, Liangqiong Qu, Qingyue Wei, Shih-Cheng Huang, Liyue Shen, Daniel L. Rubin, Lei Xing, Yuyin Zhou:
Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imaging. IEEE Trans. Medical Imaging 42(7): 1932-1943 (2023) - [c82]Siyi Tang, Jared A. Dunnmon, Liangqiong Qu, Khaled Kamal Saab, Tina Baykaner, Christopher Lee-Messer, Daniel L. Rubin:
Modeling Multivariate Biosignals With Graph Neural Networks and Structured State Space Models. CHIL 2023: 50-71 - [c81]Rogier van der Sluijs, Nandita Bhaskhar, Daniel L. Rubin, Curtis P. Langlotz, Akshay S. Chaudhari:
Exploring Image Augmentations for Siamese Representation Learning with Chest X-Rays. MIDL 2023: 444-467 - [c80]Amara Tariq, Siyi Tang, Hifza Sakhi, Leo Anthony Celi, Janice M. Newsome, Daniel L. Rubin, Hari Trivedi, Judy Gichoya, Bhavik N. Patel, Imon Banerjee:
Graph-Based Fusion of Imaging and Non-Imaging Data for Disease Trajectory Prediction. NER 2023: 1-4 - [c79]Juanma Zambrano Chaves, Nandita Bhaskhar, Maayane Attias, Jean-Benoit Delbrouck, Daniel L. Rubin, Andreas M. Loening, Curtis P. Langlotz, Akshay Chaudhari:
RaLEs: a Benchmark for Radiology Language Evaluations. NeurIPS 2023 - [c78]Ali Mirzazadeh, Florian Dubost, Maxwell Pike, Krish Maniar, Max Zuo, Christopher Lee-Messer, Daniel L. Rubin:
ATCON: Attention Consistency for Vision Models. WACV 2023: 1880-1889 - [c77]Florian Dubost, Erin Hong, Siyi Tang, Nandita Bhaskhar, Christopher Lee-Messer, Daniel L. Rubin:
Semi-Supervised Learning for Sparsely-Labeled Sequential Data: Application to Healthcare Video Processing. WACV 2023: 1890-1899 - [i53]Rogier van der Sluijs, Nandita Bhaskhar, Daniel L. Rubin, Curtis P. Langlotz, Akshay Chaudhari:
Exploring Image Augmentations for Siamese Representation Learning with Chest X-Rays. CoRR abs/2301.12636 (2023) - [i52]Khaled Saab, Siyi Tang, Mohamed Taha, Christopher Lee-Messer, Christopher Ré, Daniel L. Rubin:
Towards trustworthy seizure onset detection using workflow notes. CoRR abs/2306.08728 (2023) - 2022
- [j98]Amara Tariq, Omar Kallas, Patricia C. Balthazar, Scott Jeffery Lee, Terry Desser, Daniel L. Rubin, Judy Wawira Gichoya, Imon Banerjee:
Transfer language space with similar domain adaptation: a case study with hepatocellular carcinoma. J. Biomed. Semant. 13(1): 8 (2022) - [j97]Jon André Ottesen, Darvin Yi, Elizabeth Tong, Michael Iv, Anna Latysheva, Cathrine Saxhaug, Kari Dolven Jacobsen, Åslaug Helland, Kyrre Eeg Emblem, Daniel L. Rubin, Atle Bjørnerud, Greg Zaharchuk, Endre Grøvik:
2.5D and 3D segmentation of brain metastases with deep learning on multinational MRI data. Frontiers Neuroinformatics 16 (2022) - [j96]Josh Sanyal, Daniel L. Rubin, Imon Banerjee:
A weakly supervised model for the automated detection of adverse events using clinical notes. J. Biomed. Informatics 126: 103969 (2022) - [j95]Audrey Ha, Bao H. Do, Adam L. Bartret, Charles X. Fang, Albert Hsiao, Amelie M. Lutz, Imon Banerjee, Geoffrey M. Riley, Daniel L. Rubin, Kathryn J. Stevens, Erin Wang, Shannon Wang, Christopher F. Beaulieu, Brian Hurt:
Automating Scoliosis Measurements in Radiographic Studies with Machine Learning: Comparing Artificial Intelligence and Clinical Reports. J. Digit. Imaging 35(3): 524-533 (2022) - [j94]Xiang Bai, Hanchen Wang, Liya Ma, Yongchao Xu, Jiefeng Gan, Ziwei Fan, Fan Yang, Ke Ma, Jiehua Yang, Song Bai, Chang Shu, Xinyu Zou, Renhao Huang, Changzheng Zhang, Xiaowu Liu, Dandan Tu, Chuou Xu, Wenqing Zhang, Xi Wang, Anguo Chen, Yu Zeng, Dehua Yang, Ming-Wei Wang, Nagaraj Holalkere, Neil J. Halin, Ihab R. Kamel, Jia Wu, Xuehua Peng, Xiang Wang, Jianbo Shao, Pattanasak Mongkolwat, Jianjun Zhang, Weiyang Liu, Michael Roberts, Zhongzhao Teng, Lucian Beer, Lorena Escudero Sanchez, Evis Sala, Daniel L. Rubin, Adrian Weller, Joan Lasenby, Chuansheng Zheng, Jianming Wang, Zhen Li, Carola Schönlieb, Tian Xia:
Author Correction: Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence. Nat. Mach. Intell. 4(4): 413 (2022) - [j93]Miao Zhang, Liangqiong Qu, Praveer Singh, Jayashree Kalpathy-Cramer, Daniel L. Rubin:
SplitAVG: A Heterogeneity-Aware Federated Deep Learning Method for Medical Imaging. IEEE J. Biomed. Health Informatics 26(9): 4635-4644 (2022) - [c76]Amara Tariq, Siyi Tang, Hifza Sakhi, Leo Anthony Celi, Janice M. Newsome, Daniel L. Rubin, Hari Trivedi, Judy Gichoya, Bhavik N. Patel, Imon Banerjee:
Graph-based Fusion Modeling and Explanation for Disease Trajectory Prediction. AMIA 2022 - [c75]Liangqiong Qu, Yuyin Zhou, Paul Pu Liang, Yingda Xia, Feifei Wang, Ehsan Adeli, Li Fei-Fei, Daniel L. Rubin:
Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning. CVPR 2022: 10051-10061 - [c74]Assaf Hoogi, Brian Wilcox, Yachee Gupta, Daniel L. Rubin:
Self-attention Capsule Network for Tissue Classification in Case of Challenging Medical Image Statistics. ECCV Workshops (3) 2022: 219-235 - [c73]Siyi Tang, Jared Dunnmon, Khaled Kamal Saab, Xuan Zhang, Qianying Huang, Florian Dubost, Daniel L. Rubin, Christopher Lee-Messer:
Self-Supervised Graph Neural Networks for Improved Electroencephalographic Seizure Analysis. ICLR 2022 - [c72]Louis Blankemeier, Isabel Gallegos, Juan Manuel Zambrano Chaves, David J. Maron, Alexander T. Sandhu, Fátima Rodriguez, Daniel L. Rubin, Bhavik N. Patel, Marc H. Willis, Robert D. Boutin, Akshay S. Chaudhari:
Opportunistic Incidence Prediction of Multiple Chronic Diseases from Abdominal CT Imaging Using Multi-task Learning. MICCAI (8) 2022: 309-318 - [c71]Khaled Saab, Sarah M. Hooper, Mayee F. Chen, Michael Zhang, Daniel L. Rubin, Christopher Ré:
Reducing Reliance on Spurious Features in Medical Image Classification with Spatial Specificity. MLHC 2022: 760-784 - [i51]Alexander S. Berdichevsky, Mor Peleg, Daniel L. Rubin:
Supervised Machine Learning Algorithm for Detecting Consistency between Reported Findings and the Conclusions of Mammography Reports. CoRR abs/2202.13618 (2022) - [i50]Siyi Tang, Amara Tariq, Jared Dunnmon, Umesh Sharma, Praneetha Elugunti, Daniel L. Rubin, Bhavik N. Patel, Imon Banerjee:
Multimodal spatiotemporal graph neural networks for improved prediction of 30-day all-cause hospital readmission. CoRR abs/2204.06766 (2022) - [i49]Yan-Ran Wang, Liangqiong Qu, Natasha Diba Sheybani, Xiaolong Luo, Jiangshan Wang, Kristina Elizabeth Hawk, Ashok Joseph Theruvath, Sergios Gatidis, Xuerong Xiao, Allison Pribnow, Daniel L. Rubin, Heike E. Daldrup-Link:
Masked Co-attentional Transformer reconstructs 100x ultra-fast/low-dose whole-body PET from longitudinal images and anatomically guided MRI. CoRR abs/2205.04044 (2022) - [i48]Rui Yan, Liangqiong Qu, Qingyue Wei, Shih-Cheng Huang, Liyue Shen, Daniel L. Rubin, Lei Xing, Yuyin Zhou:
Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imaging. CoRR abs/2205.08576 (2022) - [i47]Jupinder Parmar, Khaled Saab, Brian Pogatchnik, Daniel L. Rubin, Christopher Ré:
The Importance of Background Information for Out of Distribution Generalization. CoRR abs/2206.08794 (2022) - [i46]Nandita Bhaskhar, Daniel L. Rubin, Christopher Lee-Messer:
TRUST-LAPSE: An Explainable & Actionable Mistrust Scoring Framework for Model Monitoring. CoRR abs/2207.11290 (2022) - [i45]Minhaj Nur Alam, Rikiya Yamashita, Vignav Ramesh, Tejas Prabhune, Jennifer I. Lim, R. V. P. Chan, Joelle A. Hallak, Theodore Leng, Daniel L. Rubin:
Contrastive learning-based pretraining improves representation and transferability of diabetic retinopathy classification models. CoRR abs/2208.11563 (2022) - [i44]Ali Mirzazadeh, Florian Dubost, Maxwell Pike, Krish Maniar, Max Zuo, Christopher Lee-Messer, Daniel L. Rubin:
ATCON: Attention Consistency for Vision Models. CoRR abs/2210.09705 (2022) - [i43]Siyi Tang, Jared A. Dunnmon, Liangqiong Qu, Khaled Kamal Saab, Christopher Lee-Messer, Daniel L. Rubin:
Spatiotemporal Modeling of Multivariate Signals With Graph Neural Networks and Structured State Space Models. CoRR abs/2211.11176 (2022) - 2021
- [j92]Rebecca Sawyer Lee, Jared A. Dunnmon, Ann He, Siyi Tang, Christopher Ré, Daniel L. Rubin:
Comparison of segmentation-free and segmentation-dependent computer-aided diagnosis of breast masses on a public mammography dataset. J. Biomed. Informatics 113: 103656 (2021) - [j91]Yuhan Zhang, Xiwei Zhang, Zexuan Ji, Sijie Niu, Theodore Leng, Daniel L. Rubin, Songtao Yuan, Qiang Chen:
An integrated time adaptive geographic atrophy prediction model for SD-OCT images. Medical Image Anal. 68: 101893 (2021) - [j90]Xiang Bai, Hanchen Wang, Liya Ma, Yongchao Xu, Jiefeng Gan, Ziwei Fan, Fan Yang, Ke Ma, Jiehua Yang, Song Bai, Chang Shu, Xinyu Zou, Renhao Huang, Changzheng Zhang, Xiaowu Liu, Dandan Tu, Chuou Xu, Wenqing Zhang, Xi Wang, Anguo Chen, Yu Zeng, Dehua Yang, Ming-Wei Wang, Nagaraj Holalkere, Neil J. Halin, Ihab R. Kamel, Jia Wu, Xuehua Peng, Xiang Wang, Jianbo Shao, Pattanasak Mongkolwat, Jianjun Zhang, Weiyang Liu, Michael Roberts, Zhongzhao Teng, Lucian Beer, Lorena Escudero Sanchez, Evis Sala, Daniel L. Rubin, Adrian Weller, Joan Lasenby, Chuansheng Zheng, Jianming Wang, Zhen Li, Carola Schönlieb, Tian Xia:
Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence. Nat. Mach. Intell. 3(12): 1081-1089 (2021) - [j89]Endre Grøvik, Darvin Yi, Michael Iv, Elizabeth Tong, Line Brennhaug Nilsen, Anna Latysheva, Cathrine Saxhaug, Kari Dolven Jacobsen, Åslaug Helland, Kyrre Eeg Emblem, Daniel L. Rubin, Greg Zaharchuk:
Handling missing MRI sequences in deep learning segmentation of brain metastases: a multicenter study. npj Digit. Medicine 4 (2021) - [j88]Rikiya Yamashita, Jin Long, Snikitha Banda, Jeanne Shen, Daniel L. Rubin:
Learning Domain-Agnostic Visual Representation for Computational Pathology Using Medically-Irrelevant Style Transfer Augmentation. IEEE Trans. Medical Imaging 40(12): 3945-3954 (2021) - [c70]Thiago Santos, Omar Kallas, Janice M. Newsome, Daniel L. Rubin, Judy W. Gichoya, Imon Banerjee:
A Fusion NLP Model for the Inference of Standardized Thyroid Nodule Malignancy Scores from Radiology Report Text. AMIA 2021 - [c69]Jean-Benoit Delbrouck, Cassie Zhang, Daniel L. Rubin:
QIAI at MEDIQA 2021: Multimodal Radiology Report Summarization. BioNLP@NAACL-HLT 2021: 285-290 - [c68]Oliver Zhang, Jean-Benoit Delbrouck, Daniel L. Rubin:
Out of Distribution Detection for Medical Images. UNSURE/PIPPI@MICCAI 2021: 102-111 - [c67]Khaled Saab, Sarah M. Hooper, Nimit Sharad Sohoni, Jupinder Parmar, Brian Pogatchnik, Sen Wu, Jared A. Dunnmon, Hongyang R. Zhang, Daniel L. Rubin, Christopher Ré:
Observational Supervision for Medical Image Classification Using Gaze Data. MICCAI (2) 2021: 603-614 - [i42]Rikiya Yamashita, Jin Long, Snikitha Banda, Jeanne Shen, Daniel L. Rubin:
Learning domain-agnostic visual representation for computational pathology using medically-irrelevant style transfer augmentation. CoRR abs/2102.01678 (2021) - [i41]Sharut Gupta, Praveer Singh, Ken Chang, Liangqiong Qu, Mehak Aggarwal, Nishanth Thumbavanam Arun, Ashwin Vaswani, Shruti Raghavan, Vibha Agarwal, Mishka Gidwani, Katharina Hoebel, Jay B. Patel, Charles Lu, Christopher P. Bridge, Daniel L. Rubin, Jayashree Kalpathy-Cramer:
Addressing catastrophic forgetting for medical domain expansion. CoRR abs/2103.13511 (2021) - [i40]Siyi Tang, Jared A. Dunnmon, Khaled Saab, Xuan Zhang, Qianying Huang, Florian Dubost, Daniel L. Rubin, Christopher Lee-Messer:
Automated Seizure Detection and Seizure Type Classification From Electroencephalography With a Graph Neural Network and Self-Supervised Pre-Training. CoRR abs/2104.08336 (2021) - [i39]Vignav Ramesh, Blaine Rister, Daniel L. Rubin:
COVID-19 Lung Lesion Segmentation Using a Sparsely Supervised Mask R-CNN on Chest X-rays Automatically Computed from Volumetric CTs. CoRR abs/2105.08147 (2021) - [i38]Florian Dubost, Khaled Kamal Saab, Erin Hong, Daniel Yang Fu, Max Pike, Siddharth Sharma, Siyi Tang, Nandita Bhaskhar, Christopher Lee-Messer, Daniel L. Rubin:
Double Descent Optimization Pattern and Aliasing: Caveats of Noisy Labels. CoRR abs/2106.02100 (2021) - [i37]Liangqiong Qu, Yuyin Zhou, Paul Pu Liang, Yingda Xia, Feifei Wang, Li Fei-Fei, Ehsan Adeli, Daniel L. Rubin:
Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning. CoRR abs/2106.06047 (2021) - [i36]Liangqiong Qu, Niranjan Balachandar, Miao Zhang, Daniel L. Rubin:
Handling Data Heterogeneity with Generative Replay in Collaborative Learning for Medical Imaging. CoRR abs/2106.13208 (2021) - [i35]Miao Zhang, Liangqiong Qu, Praveer Singh, Jayashree Kalpathy-Cramer, Daniel L. Rubin:
SplitAVG: A heterogeneity-aware federated deep learning method for medical imaging. CoRR abs/2107.02375 (2021) - [i34]Liangqiong Qu, Niranjan Balachandar, Daniel L. Rubin:
An Experimental Study of Data Heterogeneity in Federated Learning Methods for Medical Imaging. CoRR abs/2107.08371 (2021) - [i33]Xiang Bai, Hanchen Wang, Liya Ma, Yongchao Xu, Jiefeng Gan, Ziwei Fan, Fan Yang, Ke Ma, Jiehua Yang, Song Bai, Chang Shu, Xinyu Zou, Renhao Huang, Changzheng Zhang, Xiaowu Liu, Dandan Tu, Chuou Xu, Wenqing Zhang, Xi Wang, Anguo Chen, Yu Zeng, Dehua Yang, Ming-Wei Wang, Nagaraj Holalkere, Neil J. Halin, Ihab R. Kamel, Jia Wu, Xuehua Peng, Xiang Wang, Jianbo Shao, Pattanasak Mongkolwat, Jianjun Zhang, Weiyang Liu, Michael Roberts, Zhongzhao Teng, Lucian Beer, Lorena Escudero Sanchez, Evis Sala, Daniel L. Rubin, Adrian Weller, Joan Lasenby, Chuangsheng Zheng, Jianming Wang, Zhen Li, Carola-Bibiane Schönlieb, Tian Xia:
Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial Intelligence. CoRR abs/2111.09461 (2021) - [i32]Yuyin Zhou, Shih-Cheng Huang, Jason Alan Fries, Alaa Youssef, Timothy J. Amrhein, Marcello Chang, Imon Banerjee, Daniel L. Rubin, Lei Xing, Nigam Shah, Matthew P. Lungren:
RadFusion: Benchmarking Performance and Fairness for Multimodal Pulmonary Embolism Detection from CT and EHR. CoRR abs/2111.11665 (2021) - [i31]Siddharth Sharma, Florian Dubost, Christopher Lee-Messer, Daniel L. Rubin:
Automated Detection of Patients in Hospital Video Recordings. CoRR abs/2111.14270 (2021) - 2020
- [j87]Niranjan Balachandar, Ken Chang, Jayashree Kalpathy-Cramer, Daniel L. Rubin:
Accounting for data variability in multi-institutional distributed deep learning for medical imaging. J. Am. Medical Informatics Assoc. 27(5): 700-708 (2020) - [j86]Niranjan Balachandar, Ken Chang, Jayashree Kalpathy-Cramer, Daniel L. Rubin:
Corrigendum to: Accounting for data variability in multi-institutional distributed deep learning for medical imaging. J. Am. Medical Informatics Assoc. 27(8): 1340 (2020) - [j85]David M. Cohn, Tarub S. Mabud, Victoria A. Arendt, Andre D. Souffrant, Gyeong S. Jeon, Xiao An, William T. Kuo, Daniel Y. Sze, Lawrence V. Hofmann, Daniel L. Rubin:
Toward Data-Driven Learning Healthcare Systems in Interventional Radiology: Implementation to Evaluate Venous Stent Patency. J. Digit. Imaging 33(1): 25-36 (2020) - [j84]Nathaniel C. Swinburne, David S. Mendelson, Daniel L. Rubin:
Advancing Semantic Interoperability of Image Annotations: Automated Conversion of Non-standard Image Annotations in a Commercial PACS to the Annotation and Image Markup. J. Digit. Imaging 33(1): 49-53 (2020) - [j83]Edson F. Luque, Nelson J. O. Miranda, Daniel L. Rubin, Dilvan A. Moreira:
Automatic Staging of Cancer Tumors Using AIM Image Annotations and Ontologies. J. Digit. Imaging 33(2): 287-303 (2020) - [j82]Khaled Saab, Jared Dunnmon, Christopher Ré, Daniel L. Rubin, Christopher Lee-Messer:
Weak supervision as an efficient approach for automated seizure detection in electroencephalography. npj Digit. Medicine 3 (2020) - [j81]Jared A. Dunnmon, Alexander J. Ratner, Khaled Saab, Nishith Khandwala, Matthew Markert, Hersh Sagreiya, Roger E. Goldman, Christopher Lee-Messer, Matthew P. Lungren, Daniel L. Rubin, Christopher Ré:
Cross-Modal Data Programming Enables Rapid Medical Machine Learning. Patterns 1(2): 100019 (2020) - [j80]Assaf Hoogi, Arjun Mishra, Francisco Gimenez, Jeffrey Dong, Daniel L. Rubin:
Natural Language Generation Model for Mammography Reports Simulation. IEEE J. Biomed. Health Informatics 24(9): 2711-2717 (2020) - [j79]Xiao Ma, Zexuan Ji, Sijie Niu, Theodore Leng, Daniel L. Rubin, Qiang Chen:
MS-CAM: Multi-Scale Class Activation Maps for Weakly-Supervised Segmentation of Geographic Atrophy Lesions in SD-OCT Images. IEEE J. Biomed. Health Informatics 24(12): 3443-3455 (2020) - [c66]Jiaming Zeng, Imon Banerjee, Michael Francis Gensheimer, Daniel L. Rubin:
Cancer Treatment Classification with Electronic Medical Health Records (Student Abstract). AAAI 2020: 13981-13982 - [c65]Holger R. Roth, Ken Chang, Praveer Singh, Nir Neumark, Wenqi Li, Vikash Gupta, Sharut Gupta, Liangqiong Qu, Alvin Ihsani, Bernardo C. Bizzo, Yuhong Wen, Varun Buch, Meesam Shah, Felipe Kitamura, Matheus Mendonça, Vitor Lavor, Ahmed Harouni, Colin Compas, Jesse Tetreault, Prerna Dogra, Yan Cheng, Selnur Erdal, Richard D. White, Behrooz Hashemian, Thomas J. Schultz, Miao Zhang, Adam McCarthy, B. Min Yun, Elshaimaa Sharaf, Katharina Viktoria Hoebel, Jay B. Patel, Bryan Chen, Sean Ko, Evan Leibovitz, Etta D. Pisano, Laura Coombs, Daguang Xu, Keith J. Dreyer, Ittai Dayan, Ram C. Naidu, Mona Flores, Daniel L. Rubin, Jayashree Kalpathy-Cramer:
Federated Learning for Breast Density Classification: A Real-World Implementation. DART/DCL@MICCAI 2020: 181-191 - [c64]Darvin Yi, Endre Grøvik, Michael Iv, Elizabeth Tong, Greg Zaharchuk, Daniel L. Rubin:
Brain Metastasis Segmentation Network Trained with Robustness to Annotations with Multiple False Negatives. MIDL 2020: 867-880 - [i30]Darvin Yi, Endre Grøvik, Michael Iv, Elizabeth Tong, Greg Zaharchuk, Daniel L. Rubin:
Brain Metastasis Segmentation Network Trained with Robustness to Annotations with Multiple False Negatives. CoRR abs/2001.09501 (2020) - [i29]Darvin Yi, Endre Grøvik, Michael Iv, Elizabeth Tong, Greg Zaharchuk, Daniel L. Rubin:
Random Bundle: Brain Metastases Segmentation Ensembling through Annotation Randomization. CoRR abs/2002.09809 (2020) - [i28]Blaine Rister, Daniel L. Rubin:
Probabilistic bounds on data sensitivity in deep rectifier networks. CoRR abs/2007.06192 (2020) - [i27]Holger R. Roth, Ken Chang, Praveer Singh, Nir Neumark, Wenqi Li, Vikash Gupta, Sharut Gupta, Liangqiong Qu, Alvin Ihsani, Bernardo C. Bizzo, Yuhong Wen, Varun Buch, Meesam Shah, Felipe Kitamura, Matheus Mendonça, Vitor Lavor, Ahmed Harouni, Colin Compas, Jesse Tetreault, Prerna Dogra, Yan Cheng, Selnur Erdal, Richard D. White, Behrooz Hashemian, Thomas J. Schultz, Miao Zhang, Adam McCarthy, B. Min Yun, Elshaimaa Sharaf, Katharina Viktoria Hoebel, Jay B. Patel, Bryan Chen, Sean Ko, Evan Leibovitz, Etta D. Pisano, Laura Coombs, Daguang Xu, Keith J. Dreyer, Ittai Dayan, Ram C. Naidu, Mona Flores, Daniel L. Rubin, Jayashree Kalpathy-Cramer:
Federated Learning for Breast Density Classification: A Real-World Implementation. CoRR abs/2009.01871 (2020) - [i26]Siyi Tang, Amirata Ghorbani, Rikiya Yamashita, Sameer Rehman, Jared A. Dunnmon, James Y. Zou, Daniel L. Rubin:
Data Valuation for Medical Imaging Using Shapley Value: Application on A Large-scale Chest X-ray Dataset. CoRR abs/2010.08006 (2020) - [i25]Sharut Gupta, Praveer Singh, Ken Chang, Mehak Aggarwal, Nishanth Thumbavanam Arun, Liangqiong Qu, Katharina Hoebel, Jay B. Patel, Mishka Gidwani, Ashwin Vaswani, Daniel L. Rubin, Jayashree Kalpathy-Cramer:
The unreasonable effectiveness of Batch-Norm statistics in addressing catastrophic forgetting across medical institutions. CoRR abs/2011.08096 (2020) - [i24]Florian Dubost, Erin Hong, Daniel Y. Fu, Nandita Bhaskhar, Siyi Tang, Khaled Saab, Daniel L. Rubin, Jared Dunnmon, Christopher Lee-Messer:
Let's Hope it Works! Inaccurate Supervision of Neural Networks with Incorrect Labels: Application to Epilepsy. CoRR abs/2011.14101 (2020)
2010 – 2019
- 2019
- [j78]Imon Banerjee, Yuan Ling, Matthew C. Chen, Sadid A. Hasan, Curtis P. Langlotz, Nathaniel Moradzadeh, Brian E. Chapman, Timothy Amrhein, David A. Mong, Daniel L. Rubin, Oladimeji Farri, Matthew P. Lungren:
Comparative effectiveness of convolutional neural network (CNN) and recurrent neural network (RNN) architectures for radiology text report classification. Artif. Intell. Medicine 97: 79-88 (2019) - [j77]Rongbin Xu, Sijie Niu, Qiang Chen, Zexuan Ji, Daniel L. Rubin, Yuehui Chen:
Automated geographic atrophy segmentation for SD-OCT images based on two-stage learning model. Comput. Biol. Medicine 105: 102-111 (2019) - [j76]Menglin Wu, Xinxin Cai, Qiang Chen, Zexuan Ji, Sijie Niu, Theodore Leng, Daniel L. Rubin, Hyunjin Park:
Geographic atrophy segmentation in SD-OCT images using synthesized fundus autofluorescence imaging. Comput. Methods Programs Biomed. 182 (2019) - [j75]Imon Banerjee, Selen Bozkurt, Emel Alkim, Hersh Sagreiya, Allison W. Kurian, Daniel L. Rubin:
Automatic inference of BI-RADS final assessment categories from narrative mammography report findings. J. Biomed. Informatics 92 (2019) - [j74]Selen Bozkurt, Emel Alkim, Imon Banerjee, Daniel L. Rubin:
Automated Detection of Measurements and Their Descriptors in Radiology Reports Using a Hybrid Natural Language Processing Algorithm. J. Digit. Imaging 32(4): 544-553 (2019) - [j73]Eli M. Cahan, Tina Hernandez-Boussard, Sonoo Thadaney Israni, Daniel L. Rubin:
Putting the data before the algorithm in big data addressing personalized healthcare. npj Digit. Medicine 2 (2019) - [c63]Imon Banerjee, Miji Sofela, Timothy Amrhein, Daniel L. Rubin, Roham Zamanian, Matthew P. Lungren:
Prediction of Imaging Outcomes from Electronic Health Records: Pulmonary Embolism Case-Study. AMIA 2019 - [c62]Ron C. Li, Imon Banerjee, Daniel L. Rubin, Jonathan H. Chen:
Detecting unanticipated actions downstream from clinical decision support: a data mining approach. AMIA 2019 - [c61]Yuhan Zhang, Zexuan Ji, Sijie Niu, Theodore Leng, Daniel L. Rubin, Qiang Chen:
A Multi-Scale Deep Convolutional Neural Network For Joint Segmentation And Prediction Of Geographic Atrophy In SD-OCT Images. ISBI 2019: 565-568 - [c60]Guillaume Vanoost, Yashin Dicente Cid, Daniel L. Rubin, Adrien Depeursinge:
A lung graph model for the classification of interstitial lung diseases on CT images. Medical Imaging: Computer-Aided Diagnosis 2019: 109503H - [c59]Ali Hatamizadeh, Assaf Hoogi, Debleena Sengupta, Wuyue Lu, Brian Wilcox, Daniel L. Rubin, Demetri Terzopoulos:
Deep Active Lesion Segmentation. MLMI@MICCAI 2019: 98-105 - [c58]Khaled Saab, Jared Dunnmon, Roger E. Goldman, Alexander Ratner, Hersh Sagreiya, Christopher Ré, Daniel L. Rubin:
Doubly Weak Supervision of Deep Learning Models for Head CT. MICCAI (3) 2019: 811-819 - [i23]Imon Banerjee, Luis de Sisternes, Joelle A. Hallak, Theodore Leng, Aaron Osborne, Mary Durbin, Daniel L. Rubin:
A Deep-learning Approach for Prognosis of Age-Related Macular Degeneration Disease using SD-OCT Imaging Biomarkers. CoRR abs/1902.10700 (2019) - [i22]Endre Grøvik, Darvin Yi, Michael Iv, Elisabeth Tong, Daniel L. Rubin, Greg Zaharchuk:
Deep Learning Enables Automatic Detection and Segmentation of Brain Metastases on Multi-Sequence MRI. CoRR abs/1903.07988 (2019) - [i21]Jared Dunnmon, Alexander Ratner, Nishith Khandwala, Khaled Saab, Matthew Markert, Hersh Sagreiya, Roger E. Goldman, Christopher Lee-Messer, Matthew P. Lungren, Daniel L. Rubin, Christopher Ré:
Cross-Modal Data Programming Enables Rapid Medical Machine Learning. CoRR abs/1903.11101 (2019) - [i20]Assaf Hoogi, Brian Wilcox, Yachee Gupta, Daniel L. Rubin:
Self-Attention Capsule Networks for Image Classification. CoRR abs/1904.12483 (2019) - [i19]Ali Hatamizadeh, Assaf Hoogi, Debleena Sengupta, Wuyue Lu, Brian Wilcox, Daniel L. Rubin, Demetri Terzopoulos:
Deep Active Lesion Segmentation. CoRR abs/1908.06933 (2019) - [i18]Okyaz Eminaga, Mahmoud Abbas, Christian Kunder, Andreas M. Loening, Jeanne Shen, James D. Brooks, Curtis P. Langlotz, Daniel L. Rubin:
Plexus Convolutional Neural Network (PlexusNet): A novel neural network architecture for histologic image analysis. CoRR abs/1908.09067 (2019) - [i17]Okyaz Eminaga, Yuri Tolkach, Christian Kunder, Mahmoud Abbas, Ryan Han, Rosalie Nolley, Axel Semjonow, Martin Boegemann, Sebastian Huss, Andreas M. Loening, Robert B. West, Geoffrey A. Sonn, Richard E. Fan, Olaf Bettendorf, James D. Brooks, Daniel L. Rubin:
Deep Learning for Prostate Pathology. CoRR abs/1910.04918 (2019) - [i16]Okyaz Eminaga, Mahmoud Abbas, Yuri Tolkach, Rosalie Nolley, Christian Kunder, Axel Semjonow, Martin Boegemann, Andreas M. Loening, James D. Brooks, Daniel L. Rubin:
Biologic and Prognostic Feature Scores from Whole-Slide Histology Images Using Deep Learning. CoRR abs/1910.09100 (2019) - [i15]Darvin Yi, Endre Grøvik, Michael Iv, Elisabeth Tong, Kyrre Eeg Emblem, Line Brennhaug Nilsen, Cathrine Saxhaug, Anna Latysheva, Kari Dolven Jacobsen, Åslaug Helland, Greg Zaharchuk, Daniel L. Rubin:
MRI Pulse Sequence Integration for Deep-Learning Based Brain Metastasis Segmentation. CoRR abs/1912.08775 (2019) - [i14]Endre Grøvik, Darvin Yi, Michael Iv, Elizabeth Tong, Line Brennhaug Nilsen, Anna Latysheva, Cathrine Saxhaug, Kari Dolven Jacobsen, Åslaug Helland, Kyrre Eeg Emblem, Daniel L. Rubin, Greg Zaharchuk:
Handling Missing MRI Input Data in Deep Learning Segmentation of Brain Metastases: A Multi-Center Study. CoRR abs/1912.11966 (2019) - 2018
- [j72]Imon Banerjee, Alexis Crawley, Mythili Bhethanabotla, Heike E. Daldrup-Link, Daniel L. Rubin:
Transfer learning on fused multiparametric MR images for classifying histopathological subtypes of rhabdomyosarcoma. Comput. Medical Imaging Graph. 65: 167-175 (2018) - [j71]Bethany Percha, Yuhao Zhang, Selen Bozkurt, Daniel L. Rubin, Russ B. Altman, Curtis P. Langlotz:
Expanding a radiology lexicon using contextual patterns in radiology reports. J. Am. Medical Informatics Assoc. 25(6): 679-685 (2018) - [j70]Daniel J. Vreeman, Swapna Abhyankar, Kenneth C. Wang, Chris Carr, Beverly Collins, Daniel L. Rubin, Curtis P. Langlotz:
The LOINC RSNA radiology playbook - a unified terminology for radiology procedures. J. Am. Medical Informatics Assoc. 25(7): 885-893 (2018) - [j69]Ken Chang, Niranjan Balachandar, Carson K. Lam, Darvin Yi, James M. Brown, Andrew Beers, Bruce R. Rosen, Daniel L. Rubin, Jayashree Kalpathy-Cramer:
Distributed deep learning networks among institutions for medical imaging. J. Am. Medical Informatics Assoc. 25(8): 945-954 (2018) - [j68]Imon Banerjee, Matthew C. Chen, Matthew P. Lungren, Daniel L. Rubin:
Radiology report annotation using intelligent word embeddings: Applied to multi-institutional chest CT cohort. J. Biomed. Informatics 77: 11-20 (2018) - [j67]Anupama Gupta, Imon Banerjee, Daniel L. Rubin:
Automatic information extraction from unstructured mammography reports using distributed semantics. J. Biomed. Informatics 78: 78-86 (2018) - [j66]Imon Banerjee, Camille Kurtz, Alon Edward Devorah, Bao H. Do, Daniel L. Rubin, Christopher F. Beaulieu:
Relevance feedback for enhancing content based image retrieval and automatic prediction of semantic image features: Application to bone tumor radiographs. J. Biomed. Informatics 84: 123-135 (2018) - [j65]Sebastian Echegaray, Shaimaa Bakr, Daniel L. Rubin, Sandy Napel:
Quantitative Image Feature Engine (QIFE): an Open-Source, Modular Engine for 3D Quantitative Feature Extraction from Volumetric Medical Images. J. Digit. Imaging 31(4): 403-414 (2018) - [j64]Hakan Bulu, Dorothy A. Sippo, Janie M. Lee, Elizabeth S. Burnside, Daniel L. Rubin:
Proposing New RadLex Terms by Analyzing Free-Text Mammography Reports. J. Digit. Imaging 31(5): 596-603 (2018) - [c57]Imon Banerjee, Hailey H. Choi, Terry S. Desser, Daniel L. Rubin:
A Scalable Machine Learning Approach for Inferring Probabilistic US-LI-RADS Categorization. AMIA 2018 - [c56]Selen Bozkurt, Jung Park In, Kathleen Mary Kan, Michelle Ferrari, Daniel L. Rubin, James D. Brooks, Tina Hernandez-Boussard:
An Automated Feature Engineering for Digital Rectal Examination Documentation using Natural Language Processing. AMIA 2018 - [c55]Kun-Hsing Yu, Gerald J. Berry, Daniel L. Rubin, Christopher Ré, Russ B. Altman, Michael Snyder:
Unraveling the Molecular Basis of Lung Adenocarcinoma Dedifferentiation and Prognosis by Integrating Omics and Histopathology. AMIA 2018 - [c54]Myra Cheng, Alfiia Galimzianova, Ziga Lesjak, Ziga Spiclin, Christopher B. Lock, Daniel L. Rubin:
A Multi-scale Multiple Sclerosis Lesion Change Detection in a Multi-sequence MRI. DLMIA/ML-CDS@MICCAI 2018: 353-360 - [i13]Imon Banerjee, Michael Francis Gensheimer, Douglas J. Wood, Solomon Henry, Daniel T. Chang, Daniel L. Rubin:
Probabilistic Prognostic Estimates of Survival in Metastatic Cancer Patients (PPES-Met) Utilizing Free-Text Clinical Narratives. CoRR abs/1801.03058 (2018) - [i12]Imon Banerjee, Hailey H. Choi, Terry S. Desser, Daniel L. Rubin:
A Scalable Machine Learning Approach for Inferring Probabilistic US-LI-RADS Categorization. CoRR abs/1806.07346 (2018) - [i11]Blaine Rister, Darvin Yi, Kaushik Shivakumar, Tomomi Nobashi, Daniel L. Rubin:
CT organ segmentation using GPU data augmentation, unsupervised labels and IOU loss. CoRR abs/1811.11226 (2018) - 2017
- [j63]Xi Chen, Ross D. Shachter, Allison W. Kurian, Daniel L. Rubin:
Dynamic strategy for personalized medicine: An application to metastatic breast cancer. J. Biomed. Informatics 68: 50-57 (2017) - [j62]Kyung Hoon Hwang, Haejun Lee, Geon Koh, Debra Willrett, Daniel L. Rubin:
Building and Querying RDF/OWL Database of Semantically Annotated Nuclear Medicine Images. J. Digit. Imaging 30(1): 4-10 (2017) - [j61]Zeynettin Akkus, Alfiia Galimzianova, Assaf Hoogi, Daniel L. Rubin, Bradley J. Erickson:
Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions. J. Digit. Imaging 30(4): 449-459 (2017) - [j60]Imon Banerjee, Christopher F. Beaulieu, Daniel L. Rubin:
Computerized Prediction of Radiological Observations Based on Quantitative Feature Analysis: Initial Experience in Liver Lesions. J. Digit. Imaging 30(4): 506-518 (2017) - [j59]Assaf Hoogi, Christopher F. Beaulieu, Guilherme M. Cunha, Elhamy Heba, Claude B. Sirlin, Sandy Napel, Daniel L. Rubin:
Adaptive local window for level set segmentation of CT and MRI liver lesions. Medical Image Anal. 37: 46-55 (2017) - [j58]Blaine Rister, Daniel L. Rubin:
Piecewise convexity of artificial neural networks. Neural Networks 94: 34-45 (2017) - [j57]Sijie Niu, Qiang Chen, Luis de Sisternes, Zexuan Ji, Ze Ming Zhou, Daniel L. Rubin:
Robust noise region-based active contour model via local similarity factor for image segmentation. Pattern Recognit. 61: 104-119 (2017) - [j56]Blaine Rister, Mark A. Horowitz, Daniel L. Rubin:
Volumetric Image Registration From Invariant Keypoints. IEEE Trans. Image Process. 26(10): 4900-4910 (2017) - [j55]Karim Lekadir, Alfiia Galimzianova, Àngels Betriu, Maria del Mar Vila, Laura Igual, Daniel L. Rubin, Elvira Fernández, Petia Radeva, Sandy Napel:
A Convolutional Neural Network for Automatic Characterization of Plaque Composition in Carotid Ultrasound. IEEE J. Biomed. Health Informatics 21(1): 48-55 (2017) - [j54]Assaf Hoogi, Arjun Subramaniam, Rishi Veerapaneni, Daniel L. Rubin:
Adaptive Estimation of Active Contour Parameters Using Convolutional Neural Networks and Texture Analysis. IEEE Trans. Medical Imaging 36(3): 781-791 (2017) - [c53]Imon Banerjee, Sriraman Madhavan, Roger Eric Goldman, Daniel L. Rubin:
Intelligent Word Embeddings of Free-Text Radiology Reports. AMIA 2017 - [c52]Alfiia Galimzianova, Sean M. Siebert, Aya Kamaya, Terry S. Desser, Daniel L. Rubin:
Toward Automated Pre-Biopsy Thyroid Cancer Risk Estimation in Ultrasound. AMIA 2017 - [c51]Zeshan Hussain, Francisco Gimenez, Darvin Yi, Daniel L. Rubin:
Differential Data Augmentation Techniques for Medical Imaging Classification Tasks. AMIA 2017 - [c50]Daniel J. Vreeman, Ken Wang, Chris Carr, Beverly Collins, Swapna Abhyankar, Jamalynne Deckard, Clement J. McDonald, Daniel L. Rubin, Curtis P. Langlotz:
The LOINC/RSNA Radiology Playbook: A unified terminology for radiology procedures. AMIA 2017 - [c49]Kun-Hsing Yu, Ce Zhang, Gerald J. Berry, Russ B. Altman, Christopher Ré, Daniel L. Rubin, Michael Snyder:
Predicting Non-Small Cell Lung Cancer Diagnosis and Prognosis by Fully Automated Microscopic Pathology Image Features. AMIA 2017 - [c48]Andriy Fedorov, Daniel L. Rubin, David A. Clunie, Steve Pieper, Ron Kikinis:
Standardized communications of quantitative image analysis results using DICOM: Establishing interoperability through outreach and community engagement. CRI 2017 - [c47]Paroma Varma, Bryan D. He, Payal Bajaj, Nishith Khandwala, Imon Banerjee, Daniel L. Rubin, Christopher Ré:
Inferring Generative Model Structure with Static Analysis. NIPS 2017: 240-250 - [i10]Assaf Hoogi, John W. Lambert, Yefeng Zheng, Dorin Comaniciu, Daniel L. Rubin:
A Fully-Automated Pipeline for Detection and Segmentation of Liver Lesions and Pathological Lymph Nodes. CoRR abs/1703.06418 (2017) - [i9]Darvin Yi, Rebecca Lynn Sawyer, David Cohn III, Jared Dunnmon, Carson K. Lam, Xuerong Xiao, Daniel L. Rubin:
Optimizing and Visualizing Deep Learning for Benign/Malignant Classification in Breast Tumors. CoRR abs/1705.06362 (2017) - [i8]Paroma Varma, Bryan D. He, Payal Bajaj, Imon Banerjee, Nishith Khandwala, Daniel L. Rubin, Christopher Ré:
Inferring Generative Model Structure with Static Analysis. CoRR abs/1709.02477 (2017) - [i7]Ken Chang, Niranjan Balachandar, Carson K. Lam, Darvin Yi, James M. Brown, Andrew Beers, Bruce R. Rosen, Daniel L. Rubin, Jayashree Kalpathy-Cramer:
Institutionally Distributed Deep Learning Networks. CoRR abs/1709.05929 (2017) - [i6]Imon Banerjee, Sriraman Madhavan, Roger Eric Goldman, Daniel L. Rubin:
Intelligent Word Embeddings of Free-Text Radiology Reports. CoRR abs/1711.06968 (2017) - 2016
- [j53]Samuel G. Finlayson, Mia A. Levy, Sunil Reddy, Daniel L. Rubin:
Toward rapid learning in cancer treatment selection: An analytical engine for practice-based clinical data. J. Biomed. Informatics 60: 104-113 (2016) - [j52]Selen Bozkurt, Francisco Gimenez, Elizabeth S. Burnside, Kemal Hakan Gülkesen, Daniel L. Rubin:
Using automatically extracted information from mammography reports for decision-support. J. Biomed. Informatics 62: 224-231 (2016) - [j51]Jocelyn Barker, Assaf Hoogi, Adrien Depeursinge, Daniel L. Rubin:
Automated classification of brain tumor type in whole-slide digital pathology images using local representative tiles. Medical Image Anal. 30: 60-71 (2016) - [j50]Idit Diamant, Assaf Hoogi, Christopher F. Beaulieu, Mustafa Safdari, Eyal Klang, Michal Amitai, Hayit Greenspan, Daniel L. Rubin:
Improved Patch-Based Automated Liver Lesion Classification by Separate Analysis of the Interior and Boundary Regions. IEEE J. Biomed. Health Informatics 20(6): 1585-1594 (2016) - [j49]Pol Cirujeda, Yashin Dicente Cid, Henning Müller, Daniel L. Rubin, Todd A. Aguilera, Billy W. Loo, Maximilian Diehn, Xavier Binefa, Adrien Depeursinge:
A 3-D Riesz-Covariance Texture Model for Prediction of Nodule Recurrence in Lung CT. IEEE Trans. Medical Imaging 35(12): 2620-2630 (2016) - [i5]Assaf Hoogi, Christopher F. Beaulieu, Guilherme M. Cunha, Elhamy Heba, Claude B. Sirlin, Sandy Napel, Daniel L. Rubin:
Adaptive Local Window for Level Set Segmentation of CT and MRI Liver Lesions. CoRR abs/1606.03765 (2016) - [i4]Imon Banerjee, Lewis Hahn, Geoffrey A. Sonn, Richard E. Fan, Daniel L. Rubin:
Computerized Multiparametric MR image Analysis for Prostate Cancer Aggressiveness-Assessment. CoRR abs/1612.00408 (2016) - 2015
- [j48]Selen Bozkurt, Jafi A. Lipson, Utku Senol, Daniel L. Rubin:
Automatic abstraction of imaging observations with their characteristics from mammography reports. J. Am. Medical Informatics Assoc. 22(e1): e81-e92 (2015) - [j47]Maria De-Arteaga, Ivan Eggel, Bao H. Do, Daniel L. Rubin, Charles E. Kahn Jr., Henning Müller:
Comparing image search behaviour in the ARRS GoldMiner search engine and a clinical PACS/RIS. J. Biomed. Informatics 56: 57-64 (2015) - [j46]Qiang Chen, Luis de Sisternes, Theodore Leng, Daniel L. Rubin:
Application of Improved Homogeneity Similarity-Based Denoising in Optical Coherence Tomography Retinal Images. J. Digit. Imaging 28(3): 346-361 (2015) - [c46]Mehmet Günhan Ertosun, Daniel L. Rubin:
Automated Grading of Gliomas using Deep Learning in Digital Pathology Images: A modular approach with ensemble of convolutional neural networks. AMIA 2015 - [c45]Daniel L. Rubin:
ePAD: Leveraging image data in learning healthcare systems. AMIA 2015 - [c44]Luis de Sisternes, Joseph Rothstein, Abra Jeffers, Weiva Sieh, Daniel L. Rubin:
Polychromatic X-Ray Absorptiometry to Quantify Breast Density Volume, Ratio and their Associated Breast Cancer Risk in Full-Digital Mammography. AMIA 2015 - [c43]Mehmet Günhan Ertosun, Daniel L. Rubin:
Probabilistic visual search for masses within mammography images using deep learning. BIBM 2015: 1310-1315 - [c42]Dilvan A. Moreira, Cleber Hage, Edson F. Luque, Debra Willrett, Daniel L. Rubin:
3D Markup of Radiological Images in ePAD, a Web-Based Image Annotation Tool. CBMS 2015: 97-102 - [c41]Edson F. Luque, Daniel L. Rubin, Dilvan A. Moreira:
Automatic Classification of Cancer Tumors Using Image Annotations and Ontologies. CBMS 2015: 368-369 - [c40]Camille Kurtz, Paul-André Idoux, Avinash Thangali, Florence Cloppet, Christopher F. Beaulieu, Daniel L. Rubin:
Semantic Retrieval of Radiological Images with Relevance Feedback. MRDM@ECIR 2015: 11-25 - [c39]Yixuan Yuan, Assaf Hoogi, Christopher F. Beaulieu, Max Q.-H. Meng, Daniel L. Rubin:
Weighted locality-constrained linear coding for lesion classification in CT images. EMBC 2015: 6362-6365 - [c38]Pol Cirujeda, Henning Müller, Daniel L. Rubin, Todd A. Aguilera, Billy W. Loo, Maximilian Diehn, Xavier Binefa, Adrien Depeursinge:
3D Riesz-wavelet based Covariance descriptors for texture classification of lung nodule tissue in CT. EMBC 2015: 7909-7912 - [c37]Adrien Depeursinge, Pedram Pad, Anne S. Chin, Ann N. Leung, Daniel L. Rubin, Henning Müller, Michael Unser:
Optimized steerable wavelets for texture analysis of lung tissue in 3-D CT: Classification of usual interstitial pneumonia. ISBI 2015: 403-406 - 2014
- [j45]B. Nolan Nichols, José L. V. Mejino Jr., Landon Detwiler, Trond T. Nilsen, Maryann E. Martone, Jessica A. Turner, Daniel L. Rubin, James F. Brinkley:
Neuroanatomical domain of the foundational model of anatomy ontology. J. Biomed. Semant. 5: 1 (2014) - [j44]Sijie Niu, Qiang Chen, Luis de Sisternes, Daniel L. Rubin, Weiwei Zhang, Qinghuai Liu:
Automated retinal layers segmentation in SD-OCT images using dual-gradient and spatial correlation smoothness constraint. Comput. Biol. Medicine 54: 116-128 (2014) - [j43]Aaron Adcock, Daniel L. Rubin, Gunnar E. Carlsson:
Classification of hepatic lesions using the matching metric. Comput. Vis. Image Underst. 121: 36-42 (2014) - [j42]Camille Kurtz, Christopher F. Beaulieu, Sandy Napel, Daniel L. Rubin:
A hierarchical knowledge-based approach for retrieving similar medical images described with semantic annotations. J. Biomed. Informatics 49: 227-244 (2014) - [j41]Pattanasak Mongkolwat, Vladimir Kleper, Skip Talbot, Daniel L. Rubin:
The National Cancer Informatics Program (NCIP) Annotation and Image Markup (AIM) Foundation Model. J. Digit. Imaging 27(6): 692-701 (2014) - [j40]Camille Kurtz, Adrien Depeursinge, Sandy Napel, Christopher F. Beaulieu, Daniel L. Rubin:
On combining image-based and ontological semantic dissimilarities for medical image retrieval applications. Medical Image Anal. 18(7): 1082-1100 (2014) - [j39]Adrien Depeursinge, Camille Kurtz, Christopher F. Beaulieu, Sandy Napel, Daniel L. Rubin:
Predicting Visual Semantic Descriptive Terms From Radiological Image Data: Preliminary Results With Liver Lesions in CT. IEEE Trans. Medical Imaging 33(8): 1669-1676 (2014) - [c36]Francisco Gimenez, Yirong Wu, Elizabeth S. Burnside, Daniel L. Rubin:
A Novel Method to Assess Incompleteness of Mammography Report Content. AMIA 2014 - [c35]Camille Kurtz, Daniel L. Rubin:
Utilisation de relations ontologiques pour la comparaison d'images décrites par des annotations sémantiques. EGC 2014: 609-614 - [c34]Camille Kurtz, Adrien Depeursinge, Christopher F. Beaulieu, Daniel L. Rubin:
A semantic framework for the retrieval of similar radiological images based on medical annotations. ICIP 2014: 2241-2245 - [c33]Selen Bozkurt, Daniel L. Rubin:
Extracting Imaging Observation Entities in Mammography Reports. MIE 2014: 1223 - [i3]Balasubramanian Narasimhan, Daniel L. Rubin, Samuel M. Gross, Marina Bendersky, Philip W. Lavori:
Software for Distributed Computation on Medical Databases: A Demonstration Project. CoRR abs/1412.6890 (2014) - 2013
- [j38]Qiang Chen, Fang Quan, Jiajing Xu, Daniel L. Rubin:
Snake model-based lymphoma segmentation for sequential CT images. Comput. Methods Programs Biomed. 111(2): 366-375 (2013) - [j37]Daniel I. Golden, Jafi A. Lipson, Melinda L. Telli, James M. Ford, Daniel L. Rubin:
Research and applications: Dynamic contrast-enhanced MRI-based biomarkers of therapeutic response in triple-negative breast cancer. J. Am. Medical Informatics Assoc. 20(6): 1059-1066 (2013) - [j36]Kalpana M. Kanal, Elizabeth A. Krupinski, Eric A. Berns, William R. Geiser, Andrew Karellas, Martha B. Mainiero, Melissa C. Martin, Samir B. Patel, Daniel L. Rubin, Jon D. Shepard, Eliot L. Siegel, Judith A. Wolfman, Tariq A. Mian, Mary C. Mahoney, Margaret Wyatt:
ACR-AAPM-SIIM Practice Guideline for Determinants of Image Quality in Digital Mammography. J. Digit. Imaging 26(1): 10-25 (2013) - [j35]Andrew J. Buckler, Matt Ouellette, Jovanna Danagoulian, Gary Wernsing, Tiffany Ting Liu, Erica S. Savig, Baris E. Suzek, Daniel L. Rubin, David Paik:
Quantitative Imaging Biomarker Ontology (QIBO) for Knowledge Representation of Biomedical Imaging Biomarkers. J. Digit. Imaging 26(4): 630-641 (2013) - [j34]Andrew J. Buckler, Tiffany Ting Liu, Erica S. Savig, Baris E. Suzek, Daniel L. Rubin, David Paik:
Erratum to: Quantitative Imaging Biomarker Ontology (QIBO) for Knowledge Representation of Biomedical Imaging Biomarkers. J. Digit. Imaging 26(4): 642 (2013) - [j33]Jessica S. Faruque, Daniel L. Rubin, Christopher F. Beaulieu, Sandy Napel:
Modeling Perceptual Similarity Measures in CT Images of Focal Liver Lesions. J. Digit. Imaging 26(4): 714-720 (2013) - [j32]Qiang Chen, Theodore Leng, Luoluo Zheng, Lauren Kutzscher, Jeffrey J. Ma, Luis de Sisternes, Daniel L. Rubin:
Automated drusen segmentation and quantification in SD-OCT images. Medical Image Anal. 17(8): 1058-1072 (2013) - [c32]Kun-Hsing Yu, Shanshan Tuo, Daniel L. Rubin:
Classifying Benign and Malignant Lung Diseases by Applying Machine Learning Methods to Microscopic Pathology Images. AMIA 2013 - [c31]Selen Bozkurt, Kemal Hakan Gülkesen, Daniel L. Rubin:
Annotation for Information Extraction from Mammography Reports. ICIMTH 2013: 183-185 - [c30]Mustafa Safdari, Raghav Pasari, Daniel L. Rubin, Hayit Greenspan:
Image patch-based method for automated classification and detection of focal liver lesions on CT. Medical Imaging: Computer-Aided Diagnosis 2013: 86700Y - 2012
- [j31]Bethany Percha, Houssam Nassif, Jafi A. Lipson, Elizabeth S. Burnside, Daniel L. Rubin:
Automatic classification of mammography reports by BI-RADS breast tissue composition class. J. Am. Medical Informatics Assoc. 19(5): 913-916 (2012) - [j30]Matthew W. Shore, Daniel L. Rubin, Charles E. Kahn Jr.:
Integration of Imaging Signs into RadLex. J. Digit. Imaging 25(1): 50-55 (2012) - [j29]Jiajing Xu, Jessica S. Faruque, Christopher F. Beaulieu, Daniel L. Rubin, Sandy Napel:
A Comprehensive Descriptor of Shape: Method and Application to Content-Based Retrieval of Similar Appearing Lesions in Medical Images. J. Digit. Imaging 25(1): 121-128 (2012) - [c29]Francisco Gimenez, Jiajing Xu, Yi Liu, Tiffany Ting Liu, Christopher F. Beaulieu, Daniel L. Rubin, Sandy Napel:
Automatic Annotation of Radiological Observations in Liver CT Images. AMIA 2012 - [c28]Kleberson J. A. Serique, Alan Snyder, Debra Willrett, Daniel L. Rubin, Dilvan A. Moreira:
Using the Semantic Web and Web Apps to Connect Radiologists and Oncologists. WETICE 2012: 480-485 - [i2]Yi-Hao Kao, Benjamin Van Roy, Daniel L. Rubin, Jiajing Xu, Jessica S. Faruque, Sandy Napel:
A Hybrid Method for Distance Metric Learning. CoRR abs/1206.7112 (2012) - [i1]Aaron Adcock, Daniel L. Rubin, Gunnar E. Carlsson:
Classification of Hepatic Lesions using the Matching Metric. CoRR abs/1210.0866 (2012) - 2011
- [j28]Jessica D. Tenenbaum, Patricia L. Whetzel, Kent Anderson, Charles D. Borromeo, Ivo D. Dinov, Davera Gabriel, Beth A. Kirschner, Barbara Mirel, Timothy D. Morris, Natasha Fridman Noy, Csongor Nyulas, David Rubenson, Paul R. Saxman, Harpreet Singh, Nancy Whelan, Zach Wright, Brian D. Athey, Michael J. Becich, Geoffrey S. Ginsburg, Mark A. Musen, Kevin A. Smith, Alice F. Tarantal, Daniel L. Rubin, Peter Lyster:
The Biomedical Resource Ontology (BRO) to enable resource discovery in clinical and translational research. J. Biomed. Informatics 44(1): 137-145 (2011) - [j27]Samson W. Tu, Mor Peleg, Simona Carini, Michael Bobak, Jessica Ross, Daniel L. Rubin, Ida Sim:
A practical method for transforming free-text eligibility criteria into computable criteria. J. Biomed. Informatics 44(2): 239-250 (2011) - [j26]Daniel L. Rubin, Adam E. Flanders, Woojin Kim, Khan M. Siddiqui, Charles E. Kahn:
Ontology-Assisted Analysis of Web Queries to Determine the Knowledge Radiologists Seek. J. Digit. Imaging 24(1): 160-164 (2011) - [j25]Ceyhun Burak Akgül, Daniel L. Rubin, Sandy Napel, Christopher F. Beaulieu, Hayit Greenspan, Burak Acar:
Content-Based Image Retrieval in Radiology: Current Status and Future Directions. J. Digit. Imaging 24(2): 208-222 (2011) - [j24]Andrew S. Wu, Bao H. Do, Jinsuh Kim, Daniel L. Rubin:
Evaluation of Negation and Uncertainty Detection and its Impact on Precision and Recall in Search. J. Digit. Imaging 24(2): 234-242 (2011) - [j23]Daniel Korenblum, Daniel L. Rubin, Sandy Napel, Cesar Rodriguez, Christopher F. Beaulieu:
Managing Biomedical Image Metadata for Search and Retrieval of Similar Images. J. Digit. Imaging 24(4): 739-748 (2011) - [c27]Jessica S. Faruque, Daniel L. Rubin, Christopher F. Beaulieu, Jarrett Rosenberg, Aya Kamaya, Grace Tye, Sandy Napel, Ronald M. Summers:
A Scalable Reference Standard of Visual Similarity for a Content-Based Image Retrieval System. HISB 2011: 158-165 - [c26]Francisco Gimenez, Jiajing Xu, Yi Liu, Tiffany Ting Liu, Christopher F. Beaulieu, Daniel L. Rubin, Sandy Napel:
On the Feasibility of Predicting Radiological Observations from Computational Imaging Features of Liver Lesions in CT Scans. HISB 2011: 346-350 - 2010
- [j22]Jessica A. Turner, José L. V. Mejino Jr., James F. Brinkley, Landon T. Detwiler, Hyo Jong Lee, Maryann E. Martone, Daniel L. Rubin:
Application of neuroanatomical ontologies for neuroimaging data annotation. Frontiers Neuroinformatics 4: 10 (2010) - [j21]David S. Channin, Pattanasak Mongkolwat, Vladimir Kleper, Kastubh Sepukar, Daniel L. Rubin:
The caBIGTM Annotation and Image Markup Project. J. Digit. Imaging 23(2): 217-225 (2010) - [j20]Lee A. D. Cooper, Jun Kong, David A. Gutman, Fusheng Wang, Sharath R. Cholleti, Tony C. Pan, Patrick M. Widener, Ashish Sharma, Tom Mikkelsen, Adam E. Flanders, Daniel L. Rubin, Erwin G. Van Meir, Tahsin M. Kurç, Carlos Sanchez Moreno, Daniel J. Brat, Joel H. Saltz:
An Integrative Approach for In Silico Glioma Research. IEEE Trans. Biomed. Eng. 57(10): 2617-2621 (2010) - [e1]Tharam S. Dillon, Daniel L. Rubin, William M. Gallagher, Amandeep S. Sidhu, Alexey Tsymbal:
IEEE 23rd International Symposium on Computer-Based Medical Systems (CBMS 2010), Perth, Australia, October 12-15, 2010. IEEE Computer Society 2010, ISBN 978-1-4244-9167-4 [contents]
2000 – 2009
- 2009
- [j19]Daniel L. Rubin, Ion-Florin Talos, Michael Halle, Mark A. Musen, Ron Kikinis:
Computational neuroanatomy: ontology-based representation of neural components and connectivity. BMC Bioinform. 10(S-2) (2009) - [j18]Nigam H. Shah, Nipun Bhatia, Clément Jonquet, Daniel L. Rubin, Annie P. Chiang, Mark A. Musen:
Comparison of concept recognizers for building the Open Biomedical Annotator. BMC Bioinform. 10(S-9): 14 (2009) - [j17]Daniel L. Rubin, Pattanasak Mongkolwat, Vladimir Kleper, Kaustubh Supekar, David S. Channin:
Annotation and Image Markup: Accessing and Interoperating with the Semantic Content in Medical Imaging. IEEE Intell. Syst. 24(1): 57-65 (2009) - [j16]Charles E. Kahn Jr., Daniel L. Rubin:
Research Paper: Automated Semantic Indexing of Figure Captions to Improve Radiology Image Retrieval. J. Am. Medical Informatics Assoc. 16(3): 380-386 (2009) - [j15]Natalya Fridman Noy, Nigam H. Shah, Patricia L. Whetzel, Benjamin Dai, Michael Dorf, Nicholas Griffith, Clément Jonquet, Daniel L. Rubin, Margaret-Anne D. Storey, Christopher G. Chute, Mark A. Musen:
BioPortal: ontologies and integrated data resources at the click of a mouse. Nucleic Acids Res. 37(Web-Server-Issue): 170-173 (2009) - [c25]Mia A. Levy, Martin J. O'Connor, Daniel L. Rubin:
Semantic Reasoning with Image Annotations for Tumor Assessment. AMIA 2009 - 2008
- [j14]Daniel L. Rubin, Nigam Shah, Natalya Fridman Noy:
Biomedical ontologies: a functional perspective. Briefings Bioinform. 9(1): 75-90 (2008) - [j13]Graham L. Poulter, Daniel L. Rubin, Russ B. Altman, Cathal Seoighe:
MScanner: a classifier for retrieving Medline citations. BMC Bioinform. 9 (2008) - [j12]Ion-Florin Talos, Daniel L. Rubin, Michael Halle, Mark A. Musen, Ron Kikinis:
A prototype symbolic model of canonical functional neuroanatomy of the motor system. J. Biomed. Informatics 41(2): 251-263 (2008) - [j11]Daniel L. Rubin:
Creating and Curating a Terminology for Radiology: Ontology Modeling and Analysis. J. Digit. Imaging 21(4): 355-362 (2008) - [j10]William J. Bug, Giorgio A. Ascoli, Jeffrey S. Grethe, Amarnath Gupta, Christine Fennema-Notestine, Angela R. Laird, Stephen D. Larson, Daniel L. Rubin, Gordon M. Shepherd, Jessica A. Turner, Maryann E. Martone:
The NIFSTD and BIRNLex Vocabularies: Building Comprehensive Ontologies for Neuroscience. Neuroinformatics 6(3): 175-194 (2008) - [j9]Kaustubh Supekar, Vinod Menon, Daniel L. Rubin, Mark A. Musen, Michael D. Greicius:
Network Analysis of Intrinsic Functional Brain Connectivity in Alzheimer's Disease. PLoS Comput. Biol. 4(6) (2008) - [j8]Natalya Fridman Noy, Daniel L. Rubin:
Translating the Foundational Model of Anatomy into OWL. J. Web Semant. 6(2): 133-136 (2008) - [c24]Daniel L. Rubin, Dilvan A. Moreira, Pradip Kanjamala, Mark A. Musen:
BioPortal: A Web Portal to Biomedical Ontologies. AAAI Spring Symposium: Symbiotic Relationships between Semantic Web and Knowledge Engineering 2008: 74-77 - [c23]Daniel L. Rubin, Pattanasak Mongkolwat, Vladimir Kleper, Kaustubh Supekar, David S. Channin:
Medical Imaging on the Semantic Web: Annotation and Image Markup. AAAI Spring Symposium: Semantic Scientific Knowledge Integration 2008: 93-98 - [c22]Mia A. Levy, Daniel L. Rubin:
Tool Support to Enable Evaluation of the Clinical Response to Treatment. AMIA 2008 - [c21]Yueyi I. Liu, Aya Kamaya, Terry S. Desser, Daniel L. Rubin:
A Bayesian Classifier for Differentiating Benign versus Malignant Thyroid Nodules using Sonographic Features. AMIA 2008 - [c20]José L. V. Mejino Jr., Daniel L. Rubin, James F. Brinkley:
FMA-RadLex: An Application Ontology of Radiological Anatomy derived from the Foundational Model of Anatomy Reference Ontology. AMIA 2008 - [c19]Daniel L. Rubin, Cesar Rodriguez, Priyanka Shah, Christopher F. Beaulieu:
iPad: Semantic Annotation and Markup of Radiological Images. AMIA 2008 - [c18]Natalya Fridman Noy, Nigam Shah, Benjamin Dai, Michael Dorf, Nicholas Griffith, Clément Jonquet, Michael Montegut, Daniel L. Rubin, Cherie Youn, Mark A. Musen:
BioPortal: A Web Repository for Biomedical Ontologies and Data Resources. ISWC (Posters & Demos) 2008 - 2007
- [j7]Nigam H. Shah, Daniel L. Rubin, Inigo Espinosa, Kelli Montgomery, Mark A. Musen:
Annotation and query of tissue microarray data using the NCI Thesaurus. BMC Bioinform. 8 (2007) - [c17]Mia A. Levy, Ankit Garg, Aaron Tam, Yael Garten, Daniel L. Rubin:
LesionViewer: A Tool for Tracking Cancer Lesions Over Time. AMIA 2007 - [c16]Woei-Jyh Lee, Louiqa Raschid, Padmini Srinivasan, Nigam Shah, Daniel L. Rubin, Natasha Fridman Noy:
Using Annotations from Controlled Vocabularies to Find Meaningful Associations. DILS 2007: 247-263 - [c15]Kaustubh Supekar, Daniel L. Rubin, Natasha F. Noy, Mark A. Musen:
Knowledge Zone: A Public Repository of Peer-Reviewed Biomedical Ontologies. MedInfo 2007: 812-816 - 2006
- [j6]Daniel L. Rubin, Olivier Dameron, Yasser Bashir, David Grossman, Parvati Dev, Mark A. Musen:
Using ontologies linked with geometric models to reason about penetrating injuries. Artif. Intell. Medicine 37(3): 167-176 (2006) - [j5]Charles E. Kahn, David S. Channin, Daniel L. Rubin:
An Ontology for PACS Integration. J. Digit. Imaging 19(4): 316-327 (2006) - [c14]Daniel L. Rubin, David Grossman, Maxwell Lewis Neal, Daniel L. Cook, James B. Bassingthwaighte, Mark A. Musen:
Ontology-Based Representation of Simulation Models of Physiology. AMIA 2006 - [c13]Nigam H. Shah, Daniel L. Rubin, Kaustubh S. Supekar, Mark A. Musen:
Ontology-based Annotation and Query of Tissue Microarray Data. AMIA 2006 - 2005
- [j4]Daniel L. Rubin, Caroline F. Thorn, Teri E. Klein, Russ B. Altman:
Application of Information Technology: A Statistical Approach to Scanning the Biomedical Literature for Pharmacogenetics Knowledge. J. Am. Medical Informatics Assoc. 12(2): 121-129 (2005) - [j3]Mor Peleg, Daniel L. Rubin, Russ B. Altman:
Research Paper: Using Petri Net Tools to Study Properties and Dynamics of Biological Systems. J. Am. Medical Informatics Assoc. 12(2): 181-199 (2005) - [c12]Olivier Dameron, Daniel L. Rubin, Mark A. Musen:
Challenges in Converting Frame-Based Ontology into OWL: the Foundational Model of Anatomy Case-Study. AMIA 2005 - [c11]Daniel L. Rubin, Olivier Dameron, Mark A. Musen:
Use of Description Logic Classification to Reason about Consequences of Penetrating Injuries. AMIA 2005 - [c10]Daniel L. Rubin, Holger Knublauch, Ray W. Fergerson, Olivier Dameron, Mark A. Musen:
Protégé-OWL: Creating Ontology-Driven Reasoning Applications with the Web Ontology Language. AMIA 2005 - 2004
- [j2]Natalya Fridman Noy, Daniel L. Rubin, Mark A. Musen:
Making Biomedical Ontologies and Ontology Repositories Work. IEEE Intell. Syst. 19(6): 78-81 (2004) - [c9]Elizabeth S. Burnside, Daniel L. Rubin, Ross D. Shachter:
Improving a Bayesian network's ability to predict the probability of malignancy of microcalcifications on mammography. CARS 2004: 1021-1026 - [c8]Elizabeth S. Burnside, Daniel L. Rubin, Ross D. Shachter:
Using a Bayesian Network to Predict the Probability and Type of Breast Cancer Represented by Microcalcifications on Mammography. MedInfo 2004: 13-17 - [c7]Daniel L. Rubin, Michelle Whirl Carrillo, Mark Woon, John Conroy, Teri E. Klein, Russ B. Altman:
A Resource to Acquire and Summarize Pharmacogenetics Knowledge in the Literature. MedInfo 2004: 793-797 - 2002
- [j1]Micheal Hewett, Diane E. Oliver, Daniel L. Rubin, Katrina L. Easton, Joshua M. Stuart, Russ B. Altman, Teri E. Klein:
PharmGKB: the Pharmacogenetics Knowledge Base. Nucleic Acids Res. 30(1): 163-165 (2002) - [c6]Daniel L. Rubin, Farhad Shafa, Diane E. Oliver, Micheal Hewett, Russ B. Altman:
Representing genetic sequence data for pharmacogenomics: an evolutionary approach using ontological and relational models. ISMB 2002: 207-215 - [c5]Diane E. Oliver, Daniel L. Rubin, Joshua M. Stuart, Micheal Hewett, Teri E. Klein, Russ B. Altman:
Ontology Development for a Pharmacogenetics Knowledge Base. Pacific Symposium on Biocomputing 2002: 65-76 - [c4]Daniel L. Rubin, Micheal Hewett, Diane E. Oliver, Teri E. Klein, Russ B. Altman:
Automating Data Acquisition into Ontologies from Pharmacogenetics Relational Data Sources Using Declarative Object Definitions and XML. Pacific Symposium on Biocomputing 2002: 88-99 - 2000
- [c3]Elizabeth S. Burnside, Daniel L. Rubin, Ross D. Shachter:
A Bayesian network for mammography. AMIA 2000 - [c2]Daniel L. Rubin, John H. Gennari, Mark A. Musen:
Knowledge representation and tool support for critiquing clinical trial protocols. AMIA 2000
1990 – 1999
- 1999
- [c1]Daniel L. Rubin, John H. Gennari, Sandra Srinivas, Allen Yuen, Herbert Kaizer, Mark A. Musen, John S. Silva:
Tool support for authoring eligibility criteria for cancer trials. AMIA 1999
Coauthor Index
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