Raghav Mehta

According to our database, Raghav Mehta authored at least 24 papers between 2016 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

Evaluating the Fairness of Deep Learning Uncertainty Estimates in Medical Image Analysis.

Raghav Mehta, Changjian Shui, Tal Arbel

Proceedings of the Medical Imaging with Deep Learning, 2023

Mitigating Calibration Bias Without Fixed Attribute Grouping for Improved Fairness in Medical Imaging Analysis.

Changjian Shui, Justin Szeto, Raghav Mehta, Douglas L. Arnold, Tal Arbel

Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2023, 2023

Improving Image-Based Precision Medicine with Uncertainty-Aware Causal Models.

Joshua Durso-Finley, Jean-Pierre R. Falet, Raghav Mehta, Douglas L. Arnold, Nick Pawlowski, Tal Arbel

Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2023, 2023

Confusing Large Models by Confusing Small Models.

Vítor Albiero, Raghav Mehta, Ivan Evtimov, Samuel J. Bell, Levent Sagun, Aram Markosyan

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

Debiasing Counterfactuals in the Presence of Spurious Correlations.

Amar Kumar, Nima Fathi, Raghav Mehta, Brennan Nichyporuk, Jean-Pierre R. Falet, Sotirios A. Tsaftaris, Tal Arbel

Proceedings of the Clinical Image-Based Procedures, Fairness of AI in Medical Imaging, and Ethical and Philosophical Issues in Medical Imaging, 2023

2022

Propagating Uncertainty Across Cascaded Medical Imaging Tasks for Improved Deep Learning Inference.

Raghav Mehta, Thomas Christinck, Tanya Nair, Aurélie Bussy, Swapna Premasiri, Manuela Costantino, M. Mallar Chakravarthy, Douglas L. Arnold, Yarin Gal, Tal Arbel

IEEE Trans. Medical Imaging, 2022

You Only Need a Good Embeddings Extractor to Fix Spurious Correlations.

Raghav Mehta, Vítor Albiero, Li Chen, Ivan Evtimov, Tamar Glaser, Zhiheng Li, Tal Hassner

CoRR, 2022

Rethinking Generalization: The Impact of Annotation Style on Medical Image Segmentation.

Brennan Nichyporuk, Jillian Cardinell, Justin Szeto, Raghav Mehta, Jean-Pierre R. Falet, Douglas L. Arnold, Sotirios A. Tsaftaris, Tal Arbel

CoRR, 2022

Information Gain Sampling for Active Learning in Medical Image Classification.

Raghav Mehta, Changjian Shui, Brennan Nichyporuk, Tal Arbel

Proceedings of the Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, 2022

2021

QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Metrics and Benchmarking Results.

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

CoRR, 2021

Sub-cortical structure segmentation database for young population.

Jayanthi Sivaswamy, Alphin J. Thottupattu, Mythri V, Raghav Mehta, R. Sheelakumari, Chandrasekharan Kesavadas

CoRR, 2021

HAD-Net: A Hierarchical Adversarial Knowledge Distillation Network for Improved Enhanced Tumour Segmentation Without Post-Contrast Images.

Saverio Vadacchino, Raghav Mehta, Nazanin Mohammadi Sepahvand, Brennan Nichyporuk, James J. Clark, Tal Arbel

Proceedings of the Medical Imaging with Deep Learning, 7-9 July 2021, Lübeck, Germany., 2021

Cohort Bias Adaptation in Aggregated Datasets for Lesion Segmentation.

Brennan Nichyporuk, Jillian Cardinell, Justin Szeto, Raghav Mehta, Sotirios A. Tsaftaris, Douglas L. Arnold, Tal Arbel

Proceedings of the Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse Global Health, 2021

2020

Uncertainty Evaluation Metric for Brain Tumour Segmentation.

Raghav Mehta, Angelos Filos, Yarin Gal, Tal Arbel

CoRR, 2020

2019

Hybrid Cell Assignment and Sizing for Power, Area, Delay-Product Optimization of SRAM Arrays.

Ghasem Pasandi, Raghav Mehta, Massoud Pedram, Shahin Nazarian

IEEE Trans. Circuits Syst. II Express Briefs, 2019

Propagating Uncertainty Across Cascaded Medical Imaging Tasks for Improved Deep Learning Inference.

Raghav Mehta, Thomas Christinck, Tanya Nair, Paul Lemaître, Douglas L. Arnold, Tal Arbel

Proceedings of the Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Procedures, 2019

Improving Pathological Structure Segmentation via Transfer Learning Across Diseases.

Barleen Kaur, Paul Lemaître, Raghav Mehta, Nazanin Mohammadi Sepahvand, Doina Precup, Douglas L. Arnold, Tal Arbel

Proceedings of the Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data, 2019

NormCo: Deep Disease Normalization for Biomedical Knowledge Base Construction.

Dustin Wright, Yannis Katsis, Raghav Mehta, Chun-Nan Hsu

Proceedings of the 1st Conference on Automated Knowledge Base Construction, 2019

2018

3D U-Net for Brain Tumour Segmentation.

Raghav Mehta, Tal Arbel

Proceedings of the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2018

RS-Net: Regression-Segmentation 3D CNN for Synthesis of Full Resolution Missing Brain MRI in the Presence of Tumours.

Raghav Mehta, Tal Arbel

Proceedings of the Simulation and Synthesis in Medical Imaging, 2018

To Learn or Not to Learn Features for Deformable Registration?

Aabhas Majumdar, Raghav Mehta, Jayanthi Sivaswamy

Proceedings of the Understanding and Interpreting Machine Learning in Medical Image Computing Applications, 2018

High performance training of deep neural networks using pipelined hardware acceleration and distributed memory.

Raghav Mehta, Yuyang Huang, Mingxi Cheng, Shrey Bagga, Nishant Mathur, Ji Li, Jeffrey Draper, Shahin Nazarian

Proceedings of the 19th International Symposium on Quality Electronic Design, 2018

2017

M-net: A Convolutional Neural Network for deep brain structure segmentation.

Raghav Mehta, Jayanthi Sivaswamy

Proceedings of the 14th IEEE International Symposium on Biomedical Imaging, 2017

2016

A hybrid approach to tissue-based intensity standardization of brain MRI images.

Raghav Mehta, Jayanthi Sivaswamy

Proceedings of the 13th IEEE International Symposium on Biomedical Imaging, 2016