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ORCIDRaghav Mehta
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2020 – today
- 2025
[e2]Carole H. Sudre
, Raghav Mehta, Cheng Ouyang, Chen Qin, Marianne Rakic, William M. Wells III:
Uncertainty for Safe Utilization of Machine Learning in Medical Imaging - 6th International Workshop, UNSURE 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 10, 2024, Proceedings. Lecture Notes in Computer Science 15167, Springer 2025, ISBN 978-3-031-73157-0 [contents]- 2023
- [c17]Amar Kumar, Nima Fathi, Raghav Mehta, Brennan Nichyporuk, Jean-Pierre R. Falet, Sotirios A. Tsaftaris, Tal Arbel:
Debiasing Counterfactuals in the Presence of Spurious Correlations. CLIP/FAIMI/EPIMI@MICCAI 2023: 276-286 - [c16]Vítor Albiero, Raghav Mehta, Ivan Evtimov, Samuel J. Bell, Levent Sagun, Aram Markosyan:
Confusing Large Models by Confusing Small Models. ICCV (Workshops) 2023: 4306-4314 - [c15]Changjian Shui, Justin Szeto, Raghav Mehta, Douglas L. Arnold, Tal Arbel:
Mitigating Calibration Bias Without Fixed Attribute Grouping for Improved Fairness in Medical Imaging Analysis. MICCAI (3) 2023: 189-198 - [c14]Joshua Durso-Finley, Jean-Pierre R. Falet, Raghav Mehta, Douglas L. Arnold, Nick Pawlowski, Tal Arbel:
Improving Image-Based Precision Medicine with Uncertainty-Aware Causal Models. MICCAI (5) 2023: 472-481 - [c13]Raghav Mehta, Changjian Shui, Tal Arbel:
Evaluating the Fairness of Deep Learning Uncertainty Estimates in Medical Image Analysis. MIDL 2023: 1453-1492 - [e1]Carole H. Sudre, Christian F. Baumgartner, Adrian V. Dalca, Raghav Mehta, Chen Qin, William M. Wells:
Uncertainty for Safe Utilization of Machine Learning in Medical Imaging - 5th International Workshop, UNSURE 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 12, 2023, Proceedings. Lecture Notes in Computer Science 14291, Springer 2023, ISBN 978-3-031-44335-0 [contents] - [i15]Raghav Mehta, Changjian Shui, Tal Arbel:
Evaluating the Fairness of Deep Learning Uncertainty Estimates in Medical Image Analysis. CoRR abs/2303.03242 (2023) - [i14]Joshua Durso-Finley, Jean-Pierre R. Falet, Raghav Mehta, Douglas L. Arnold, Nick Pawlowski, Tal Arbel:
Improving Image-Based Precision Medicine with Uncertainty-Aware Causal Models. CoRR abs/2305.03829 (2023) - [i13]Changjian Shui, Justin Szeto, Raghav Mehta, Douglas L. Arnold, Tal Arbel:
Mitigating Calibration Bias Without Fixed Attribute Grouping for Improved Fairness in Medical Imaging Analysis. CoRR abs/2307.01738 (2023) - [i12]Amar Kumar, Nima Fathi, Raghav Mehta, Brennan Nichyporuk, Jean-Pierre R. Falet, Sotirios A. Tsaftaris, Tal Arbel:
Debiasing Counterfactuals In the Presence of Spurious Correlations. CoRR abs/2308.10984 (2023) - 2022
- [j2]Raghav Mehta, Thomas Christinck, Tanya Nair, Aurélie Bussy, Swapna Premasiri, Manuela Costantino, M. Mallar Chakravarthy, Douglas L. Arnold, Yarin Gal, Tal Arbel:
Propagating Uncertainty Across Cascaded Medical Imaging Tasks for Improved Deep Learning Inference. IEEE Trans. Medical Imaging 41(2): 360-373 (2022) - [c12]Raghav Mehta, Changjian Shui, Brennan Nichyporuk, Tal Arbel:
Information Gain Sampling for Active Learning in Medical Image Classification. UNSURE@MICCAI 2022: 135-145 - [i11]Raghav Mehta, Changjian Shui, Brennan Nichyporuk, Tal Arbel:
Information Gain Sampling for Active Learning in Medical Image Classification. CoRR abs/2208.00974 (2022) - [i10]Brennan Nichyporuk, Jillian Cardinell, Justin Szeto, Raghav Mehta, Jean-Pierre R. Falet, Douglas L. Arnold, Sotirios A. Tsaftaris, Tal Arbel:
Rethinking Generalization: The Impact of Annotation Style on Medical Image Segmentation. CoRR abs/2210.17398 (2022) - [i9]Raghav Mehta, Vítor Albiero, Li Chen, Ivan Evtimov, Tamar Glaser, Zhiheng Li, Tal Hassner:
You Only Need a Good Embeddings Extractor to Fix Spurious Correlations. CoRR abs/2212.06254 (2022) - 2021
- [c11]Brennan Nichyporuk, Jillian Cardinell, Justin Szeto, Raghav Mehta, Sotirios A. Tsaftaris, Douglas L. Arnold, Tal Arbel:
Cohort Bias Adaptation in Aggregated Datasets for Lesion Segmentation. DART/FAIR@MICCAI 2021: 101-111 - [c10]Saverio Vadacchino, Raghav Mehta, Nazanin Mohammadi Sepahvand, Brennan Nichyporuk, James J. Clark, Tal Arbel:
HAD-Net: A Hierarchical Adversarial Knowledge Distillation Network for Improved Enhanced Tumour Segmentation Without Post-Contrast Images. MIDL 2021: 787-801 - [i8]Saverio Vadacchino, Raghav Mehta, Nazanin Mohammadi Sepahvand, Brennan Nichyporuk, James J. Clark, Tal Arbel:
HAD-Net: A Hierarchical Adversarial Knowledge Distillation Network for Improved Enhanced Tumour Segmentation Without Post-Contrast Images. CoRR abs/2103.16617 (2021) - [i7]Brennan Nichyporuk, Jillian Cardinell, Justin Szeto, Raghav Mehta, Sotirios A. Tsaftaris, Douglas L. Arnold, Tal Arbel:
Cohort Bias Adaptation in Aggregated Datasets for Lesion Segmentation. CoRR abs/2108.00713 (2021) - [i6]Jayanthi Sivaswamy, Alphin J. Thottupattu, Mythri V, Raghav Mehta, R. Sheelakumari, Chandrasekharan Kesavadas:
Sub-cortical structure segmentation database for young population. CoRR abs/2111.01561 (2021) - [i5]Raghav Mehta, Angelos Filos, Ujjwal Baid, Chiharu Sako, Richard McKinley, Michael Rebsamen, Katrin Dätwyler, Raphael Meier, Piotr Radojewski, Gowtham Krishnan Murugesan, Sahil S. Nalawade, Chandan Ganesh, Benjamin C. Wagner, Fang F. Yu, Baowei Fei, Ananth J. Madhuranthakam, Joseph A. Maldjian, Laura Alexandra Daza, Catalina Gómez Caballero, Pablo Arbeláez, Chengliang Dai, Shuo Wang, Hadrien Raynaud, Yuanhan Mo, Elsa D. Angelini, Yike Guo, Wenjia Bai, Subhashis Banerjee, Linmin Pei, Murat Ak, Sarahi Rosas-González, Ilyess Zemmoura, Clovis Tauber, Minh H. Vu, Tufve Nyholm, Tommy Löfstedt, Laura Mora Ballestar, Verónica Vilaplana, Hugh McHugh, Gonzalo D. Maso Talou, Alan Wang, Jay B. Patel, Ken Chang, Katharina Hoebel, Mishka Gidwani, Nishanth Thumbavanam Arun, Sharut Gupta, Mehak Aggarwal, Praveer Singh, Elizabeth R. Gerstner, Jayashree Kalpathy-Cramer, Nicolas Boutry, Alexis Huard, Lasitha Vidyaratne, Md Monibor Rahman, Khan M. Iftekharuddin, Joseph Chazalon, Élodie Puybareau, Guillaume Tochon, Jun Ma, Mariano Cabezas, Xavier Lladó, Arnau Oliver, Liliana Valencia, Sergi Valverde, Mehdi Amian, Mohammadreza Soltaninejad, Andriy Myronenko, Ali Hatamizadeh, Xue Feng, Quan Dou, Nicholas J. Tustison, Craig H. Meyer, Nisarg A. Shah, Sanjay N. Talbar, Marc-André Weber, Abhishek Mahajan, András Jakab, Roland Wiest, Hassan M. Fathallah-Shaykh, Arash Nazeri, Mikhail Milchenko, Daniel S. Marcus, Aikaterini Kotrotsou, Rivka Colen, John B. Freymann, Justin S. Kirby, Christos Davatzikos, Bjoern H. Menze, Spyridon Bakas, Yarin Gal, Tal Arbel:
QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Metrics and Benchmarking Results. CoRR abs/2112.10074 (2021) - 2020
- [i4]Raghav Mehta, Angelos Filos, Yarin Gal, Tal Arbel:
Uncertainty Evaluation Metric for Brain Tumour Segmentation. CoRR abs/2005.14262 (2020)
2010 – 2019
- 2019
- [j1]Ghasem Pasandi, Raghav Mehta, Massoud Pedram, Shahin Nazarian:
Hybrid Cell Assignment and Sizing for Power, Area, Delay-Product Optimization of SRAM Arrays. IEEE Trans. Circuits Syst. II Express Briefs 66-II(12): 2047-2051 (2019) - [c9]Dustin Wright, Yannis Katsis, Raghav Mehta, Chun-Nan Hsu:
NormCo: Deep Disease Normalization for Biomedical Knowledge Base Construction. AKBC 2019 - [c8]Raghav Mehta, Thomas Christinck, Tanya Nair, Paul Lemaître, Douglas L. Arnold, Tal Arbel:
Propagating Uncertainty Across Cascaded Medical Imaging Tasks for Improved Deep Learning Inference. UNSURE/CLIP@MICCAI 2019: 23-32 - [c7]Barleen Kaur, Paul Lemaître, Raghav Mehta, Nazanin Mohammadi Sepahvand, Doina Precup, Douglas L. Arnold, Tal Arbel:
Improving Pathological Structure Segmentation via Transfer Learning Across Diseases. DART/MIL3ID@MICCAI 2019: 90-98 - [i3]Ghasem Pasandi, Raghav Mehta, Massoud Pedram, Shahin Nazarian:
Hybrid Cell Assignment and Sizing for Power, Area, Delay Product Optimization of SRAM Arrays. CoRR abs/1902.00484 (2019) - 2018
- [c6]Raghav Mehta, Yuyang Huang, Mingxi Cheng, Shrey Bagga, Nishant Mathur, Ji Li, Jeffrey Draper, Shahin Nazarian:
High performance training of deep neural networks using pipelined hardware acceleration and distributed memory. ISQED 2018: 383-388 - [c5]Aabhas Majumdar, Raghav Mehta, Jayanthi Sivaswamy:
To Learn or Not to Learn Features for Deformable Registration? MLCN/DLF/iMIMIC@MICCAI 2018: 52-60 - [c4]Raghav Mehta, Tal Arbel:
RS-Net: Regression-Segmentation 3D CNN for Synthesis of Full Resolution Missing Brain MRI in the Presence of Tumours. SASHIMI@MICCAI 2018: 119-129 - [c3]Raghav Mehta, Tal Arbel:
3D U-Net for Brain Tumour Segmentation. BrainLes@MICCAI (2) 2018: 254-266 - [i2]Raghav Mehta, Tal Arbel:
RS-Net: Regression-Segmentation 3D CNN for Synthesis of Full Resolution Missing Brain MRI in the Presence of Tumours. CoRR abs/1807.10972 (2018) - 2017
- [c2]Raghav Mehta, Jayanthi Sivaswamy:
M-net: A Convolutional Neural Network for deep brain structure segmentation. ISBI 2017: 437-440 - [i1]Aabhas Majumdar, Raghav Mehta, Jayanthi Sivaswamy:
To Learn or Not to Learn Features for Deformable Registration? CoRR abs/1709.01057 (2017) - 2016
- [c1]Raghav Mehta, Jayanthi Sivaswamy:
A hybrid approach to tissue-based intensity standardization of brain MRI images. ISBI 2016: 95-98
Coauthor Index
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