Raghav Mehta

Research Associate (PostDoc), Imperial College London.

raghav-1.jpg

344-15, Huxley Building

South Kensington Campus

London, UK, SW7 2AZ

I am a Research Associate (PostDoc) at Imperial College London (ICL) working with Prof. Ben Glocker at BioMedIA group. My primary research is in the field of Machine Learning and Medical Image Analysis. I am broadly interested in responsible and trustworthy machine learning models for medical image analysis.

Previously, I finished my Ph.D. in Electrical & Computer Engineering at McGill University. I was supervised by Prof. Tal Arbel at Probabilistic Vision Group (PVG), Centre for Intelligent Machines (CIM), McGill University. I obtained my master’s in Electronics & Communication Engineering at International Institute of Information Technology - Hyderabad (IIIT-H). I worked under the guidence of Prof. Jayanthi Sivaswamy at Medical Image Processing (MIP) group, Centre for Visual Information Technology (CVIT), IIIT-H.

In my free time, I like to read books, hike, binge watch TV series, and sleep.

news

Jul 5, 2024 Outstanding Reviewer Award at MIDL 2024.
Jul 1, 2024 Organizing UNSURE workshop in conjunction with MICCAI 2024.
Oct 9, 2023 Best Oral Presentation Award at FAIMI 2023 workshop held in conjunction with MICCAI 2023 conference.
Oct 1, 2023 1 paper awarded student travel award and shortlisted for Best Paper award at MICCAI 2023 conference.
Jul 5, 2023 Defended my PhD thesis!! :sparkles: :smile:
May 20, 2023 1 paper early accepted (top 15%) at MICCAI 2023 conference.
Sep 20, 2022 1 paper accepted at Machine Learning for Biomedical Imaging (MELBA) journal.
Jul 19, 2022 1 paper accepted at Machine Learning for Biomedical Imaging (MELBA) journal.
Jul 9, 2022 Named in Honourable mention list for Best Reviewer at MIDL 2022.
Jul 5, 2022 Started Internship at Responsible AI team @Meta. Supervised by Dr. Ivan Evtimov and Dr. Tal Hassner.
Sep 13, 2021 1 paper accepted at Transactions on Medical Imaging (TMI).
Sep 10, 2021 Best Paper Award at DART 2021 workshop organized in conjuction with MICCAI 2021.
Jul 7, 2021 Selected as one of the top 9 reviewers at MIDL 2021. (link)
Oct 29, 2019 Master’s thesis work on First Indian Brain Atlas was featured in different Indian NEWS outlets like India Today, Zee NEWS, Times of India, Times Now, etc.
Oct 9, 2019 Best Paper Award at UNSURE 2019 workshop held in conjunction with MICCAI 2019.

selected publications

  1. QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation – Analysis of Ranking Scores and Benchmarking Results
    Raghav Mehta, Angelos Filos, Ujjwal Baid, Chiharu Sako, Richard McKinley, Michael Rebsamen, Katrin Dätwyler, Raphael Meier, Piotr Radojewski, Gowtham Krishnan Murugesan, and 82 more authors
    Machine Learning for Biomedical Imaging (MELBA) Journal, Aug 2022
  2. 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 ArnoldYarin Gal, and Tal Arbel
    IEEE Transactions on Medical Imaging (TMI), Oct 2021
  3. Construction of Indian human brain atlas
    Jayanthi Sivaswamy, Alphin J Thottupattu, Raghav Mehta, R Sheelakumari, Chandrasekharan Kesavadas, and  others
    Neurology India (NI) Journal, Jan 2019
  4. Mitigating calibration bias without fixed attribute grouping for improved fairness in medical imaging analysis
    Changjian Shui, Justin Szeto, Raghav MehtaDouglas L Arnold, and Tal Arbel
    In International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Oct 2023
    Early Acceptance
  5. Improving Image-Based Precision Medicine with Uncertainty-Aware Causal Models
    Joshua Durso-Finley, Jean-Pierre Falet, Raghav MehtaDouglas L Arnold, Nick Pawlowski, and Tal Arbel
    In International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Oct 2023
    Student Travel Award (Top 10 paper)
  6. Evaluating the Fairness of Deep Learning Uncertainty Estimates in Medical Image Analysis
    Raghav Mehta, Changjian Shui, and Tal Arbel
    In Medical Imaging with Deep Learning (MIDL) conference, Jul 2023