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ORCIDMLHC 2022: Durham, NC, USA
- Zachary C. Lipton, Rajesh Ranganath, Mark P. Sendak, Michael W. Sjoding, Serena Yeung:
Proceedings of the Machine Learning for Healthcare Conference, MLHC 2022, 5-6 August 2022, Durham, NC, USA. Proceedings of Machine Learning Research 182, PMLR 2022 - Yuhao Zhang, Hang Jiang, Yasuhide Miura, Christopher D. Manning, Curtis P. Langlotz:
Contrastive Learning of Medical Visual Representations from Paired Images and Text. 2-25 - Anthony Li, Ming Lun Ong, Chien Wei Oei, Weixiang Lian, Hwee Pin Phua, Lin Htun Htet
, Wei Yen Lim:
Unified Auto Clinical Scoring (Uni-ACS) with Interpretable ML models. 26-53 - Junwen Wang, Xin Du, Katayoun Farrahi, Mahesan Niranjan:
Deep Cascade Learning for Optimal Medical Image Feature Representation. 54-78 - Ebrahim Pourjafari, Navid Ziaei, Mohammad R. Rezaei, Amir Sameizadeh, Mohammad Shafiee, Mohammad Alavinia, Mansour Abolghasemian, Nick Sajadi:
Survival Seq2Seq: A Survival Model based on Sequence to Sequence Architecture. 79-100 - Zepeng Huo, Xiaoning Qian, Shuai Huang, Zhangyang Wang, Bobak J. Mortazavi:
Density-Aware Personalized Training for Risk Prediction in Imbalanced Medical Data. 101-122 - Shigehiko Schamoni, Michael Hagmann, Stefan Riezler:
Ensembling Neural Networks for Improved Prediction and Privacy in Early Diagnosis of Sepsis. 123-145 - Theresa Blümlein, Joel Persson, Stefan Feuerriegel:
Learning Optimal Dynamic Treatment Regimes Using Causal Tree Methods in Medicine. 146-171 - Jiacheng Zhu, Jielin Qiu, Zhuolin Yang, Douglas Weber, Michael A. Rosenberg, Emerson Liu, Bo Li, Ding Zhao:
GeoECG: Data Augmentation via Wasserstein Geodesic Perturbation for Robust Electrocardiogram Prediction. 172-197 - Weiming Ren, Ruijing Zeng, Tongzi Wu, Tianshu Zhu, Rahul G. Krishnan:
HiCu: Leveraging Hierarchy for Curriculum Learning in Automated ICD Coding. 198-223 - Xintian Han, Mark Goldstein, Rajesh Ranganath:
Survival Mixture Density Networks. 224-248 - Magda Amiridi, Gregory Darnell, Sean Jewell:
Latent Temporal Flows for Multivariate Analysis of Wearables Data. 249-269 - Yamil Vindas, Blaise Kévin Guépié, Marilys Almar, Emmanuel Roux, Philippe Delachartre:
An hybrid CNN-Transformer model based on multi-feature extraction and attention fusion mechanism for cerebral emboli classification. 270-296 - Siddharth Biswal, Peiye Zhuang, Ayis Pyrros, Nasir Siddiqui, Sanmi Koyejo, Jimeng Sun:
EMIXER: End-to-end Multimodal X-ray Generation via Self-supervision. 297-324 - Amr Farahat, Diyuan Lu, Sebastian Bauer, Valentin Neubert, Lara Sophie Costard, Felix Rosenow, Jochen Triesch:
Diagnosing Epileptogenesis with Deep Anomaly Detection. 325-342 - Trenton Chang, Michael W. Sjoding, Jenna Wiens:
Disparate Censorship & Undertesting: A Source of Label Bias in Clinical Machine Learning. 343-390 - William Boag, Mercy Oladipo, Peter Szolovits:
EHR Safari: Data is Contextual. 391-408 - Yariv Colbeci, Maya Zohar, Daniel Neimark, Dotan Asselmann, Omri Bar:
A Multi Instance Learning Approach for Critical View of Safety Detection in Laparoscopic Cholecystectomy. 409-424 - Alain Ryser, Laura Manduchi, Fabian Laumer, Holger Michel, Sven Wellmann, Julia E. Vogt:
Anomaly Detection in Echocardiograms with Dynamic Variational Trajectory Models. 425-458 - Fernanda Ribeiro, Valentina Shumovskaia, Thomas Davies, Ira Ktena:
How fair is your graph? Exploring fairness concerns in neuroimaging studies. 459-478 - Nasir Hayat, Krzysztof J. Geras, Farah E. Shamout:
MedFuse: Multi-modal fusion with clinical time-series data and chest X-ray images. 479-503 - Ricards Marcinkevics, Ece Ozkan, Julia E. Vogt:
Debiasing Deep Chest X-Ray Classifiers using Intra- and Post-processing Methods. 504-536 - Asem Alaa, Erik Mayer, Mauricio Barahona:
ICE-NODE: Integration of Clinical Embeddings with Neural Ordinary Differential Equations. 537-564 - Nazanin Moradinasab, Yash Sharma, Laura S. Shankman, Gary K. Owens, Donald E. Brown:
Weakly Supervised Deep Instance Nuclei Detection using Points Annotation in 3D Cardiovascular Immunofluorescent Images. 565-584 - Chirag Nagpal, Willa Potosnak, Artur Dubrawski:
auton-survival: an Open-Source Package for Regression, Counterfactual Estimation, Evaluation and Phenotyping with Censored Time-to-Event Data. 585-608 - Ricardo Flores, M. L. Tlachac, Ermal Toto, Elke A. Rundensteiner:
AudiFace: Multimodal Deep Learning for Depression Screening. 609-630 - Yong Huang, Rui Cao, Amir-Mohammad Rahmani:
Reinforcement Learning For Sepsis Treatment: A Continuous Action Space Solution. 631-647 - Carissa Wu, Sonali Parbhoo, Marton Havasi, Finale Doshi-Velez:
Learning Optimal Summaries of Clinical Time-series with Concept Bottleneck Models. 648-672 - Chufan Gao, Mononito Goswami, Jieshi Chen, Artur Dubrawski:
Classifying Unstructured Clinical Notes via Automatic Weak Supervision. 673-690 - Uzma Hasan, Md. Osman Gani:
KCRL: A Prior Knowledge Based Causal Discovery Framework with Reinforcement Learning. 691-714 - George-Alexandru Adam, Chun-Hao Kingsley Chang, Benjamin Haibe-Kains, Anna Goldenberg:
Error Amplification When Updating Deployed Machine Learning Models. 715-740 - Mark P. Sendak, Gaurav Sirdeshmukh, Timothy Ochoa, Hayley Premo, Linda Tang, Kira Niederhoffer, Sarah Reed, Kaivalya Deshpande, Emily Sterrett, Melissa Bauer, Laurie Snyder, Afreen Shariff, David Whellan, Jeffrey Riggio, David Gaieski, Kristin Corey, Megan Richards, Michael Gao, Marshall Nichols, Bradley Heintze, William Knechtle, William Ratliff, Suresh Balu:
Development and Validation of ML-DQA - a Machine Learning Data Quality Assurance Framework for Healthcare. 741-759 - 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. 760-784 - Tanveer F. Syeda-Mahmood, Luyao Shi:
Searching for Fine-Grained Queries in Radiology Reports Using Similarity-Preserving Contrastive Embedding. 785-799 - Intae Moon, Stefan Groha, Alexander Gusev:
SurvLatent ODE : A Neural ODE based time-to-event model with competing risks for longitudinal data improves cancer-associated Venous Thromboembolism (VTE) prediction. 800-827 - Aniek F. Markus, Peter R. Rijnbeek, Jenna Marie Reps:
Why predicting risk can't identify 'risk factors': empirical assessment of model stability in machine learning across observational health databases. 828-852 - Raphael Poulain, Mehak Gupta, Rahmatollah Beheshti:
Few-Shot Learning with Semi-Supervised Transformers for Electronic Health Records. 853-873 - Christopher M. Trombley, Mehmet Akif Gulum, Merve Ozen, Enes Esen, Melih Aksamoglu, Mehmed M. Kantardzic:
Evaluating Uncertainty-Based Deep Learning Explanations for Prostate Lesion Detection. 874-891 - Josiah Aklilu, Serena Yeung:
ALGES: Active Learning with Gradient Embeddings for Semantic Segmentation of Laparoscopic Surgical Images. 892-911
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last updated on 2024-11-08 20:18 CET by the dblp team
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