Raghav Mehta

Research Publications

Journals

  1. B. Nichyporuk* J. Cardinell*, J. Szeto, R. Mehta, J.P. Falet, D. Arnold, S. Tsaftaris, T. Arbel, “Rethinking Generalization: The Impact of Annotation Style on Medical Image Segmentation”, The Journal of Machine Learning for Biomedical Imaging (MELBA), October 2022. [preprint]

  2. R. Mehta, A. Filos, U. Baid, …, S. Bakas, Y. Gal, T. Arbel, “QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results”, The Journal of Machine Learning for Biomedical Imaging (MELBA), August 2022. [preprint] [link]

  3. R. Mehta, T. Christinck, T. Nair, A. Bussy, S. Premasiri, M. Costantino, M. Chakravarty, D. L. Arnold, Y. Gal, T. Arbel, “Propagating Uncertainty Across Cascaded Medical Imaging Tasks for Improved Deep Learning Inference”, IEEE Transactions on Medical Imaging (TMI), Volume: 41, Issue: 2, Feb. 2022 [link]

  4. J. Sivaswamy, A. Thottupattu*, R. Mehta*, R. Sheelakumari, C. Keshavdas, “Construction of Indian Human Brain Atlas”, Neurology India, 2019, 67(1), 229-234 [link]

  5. R. Mehta, A. Majumdar, J. Sivaswamy, “BrainSegNet: a convolutional neural network architecture for automated segmentation of human brain structures.”, Journal of Medical Imaging (JMI), April 2017, 4(2), p.024003. [link]

Conferences

  1. S. Vadacchino, R. Mehta, N. M. Sepahvand, B. Nichyporuk, J. J. Clark, T. Arbel, “HAD-Net: A Hierarchical Adversarial Knowledge Distillation Network for Improved Enhanced Tumour Segmentation Without Post-Contrast Images”, Medical Imaging with Deep Learning (MIDL) 2021. [link] [preprint]

  2. R. Mehta, J. Sivaswamy, “M-net: A Convolutional Neural Network for deep brain structure segmentation.”, IEEE 14th International Symposium on Biomedical Imaging (ISBI), 2017 (pp. 437-440). (oral presentation, acceptance rate ~ 19%) [link] [presentation]

  3. R. Mehta, J. Sivaswamy, “A hybrid approach to tissue-based intensity standardization of brain MRI images.” IEEE 13th International Symposium on Biomedical Imaging (ISBI), 2016 (pp. 95-98). [link] [poster]

Workshop and Challenges

  1. R. Mehta, C. Shui, B. Nichyporuk, T. Arbel, “Information Gain Sampling for Active Learning in Medical Image Classification”, Uncertainty for Safe Utilization of Machine Learning in Medical Imaging (UNSURE) Workshop held in conjunction with 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2022. [link] [preprint] [presentation] [poster]

  2. B. Nichyporuk, J. Cardinell, J. Szeto, R. Mehta, D. Arnold, S. Tsaftaris, T. Arbel, “Cohort Bias Adaptation in Federated Datasets for Lesion Segmentation”, Domain Adaptation and Representation Transfer (DART) 2021 workshop held in conjunction with 24th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2021, Lecture Notes in Computer Science, Springer, LNCS 12968, pp. 101-111, 2021 (oral presentation) [Best Paper Award] [preprint] [link] [presentation] .

  3. R. Mehta*, T. Christinck*, T. Nair, P. Lemaitre, D. Arnold, T. Arbel, “Propagating Uncertainty Across Cascaded Medical Imaging Tasks for Improved Deep Learning Inference”, Uncertainty for Safe Utilization of Machine Learning in Medical Imaging (UNSURE) Workshop held in conjunction with 22nd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2019, Lecture Notes in Computer Science, Springer, LNCS 11840, pp. 23-32, 2019 (oral presentation, acceptance rate ~ 20%) [Best Paper Award] [link] [poster] [presentation]

  4. B. Kaur, P. Lemaitre, R. Mehta, N.M. Sepahvand, D. Precup, D. Arnold, T. Arbel, “Improving Pathological Structure Segmentation Via Transfer Learning Across Diseases”, Domain Adaptation and Representation Transfer (DART): Learning Transferable, Interpretable, and Robust Representation Workshop held in conjunction with 22nd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2019, Lecture Notes in Computer Science, Springer, LNCS 11795, pp. 90-98, 2019. (oral presentation) [link] [presentation] [poster]

  5. R. Mehta, T. Arbel, “RS-Net: Regression-Segmentation 3D CNN for Synthesis of Full Resolution Missing Brain MRI in the Presence of Tumours”, Simulation and Synthesis in Medical Imaging (SASHIMI) workshop held in conjunction with 21st International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2018, Lecture Notes in Computer Science, Springer, Vol. 11037, pp. 119-129. (oral presentation, acceptance rate ~ 20%) [preprint] [link] [presentation] [extended_report]

  6. R. Mehta, T. Arbel, “3D U-net for Brain Tumour Segmentation”, Multimodal Brain Tumour Segmentation (BraTS) challenge 2018 held in conjunction with 21st International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2018, Lecture Notes in Computer Science, Springer, LNCS 11384, pp. 254-266, 2018. [link] [poster]

  7. A. Majumdar*, R. Mehta*, J. Sivaswamy, “To Learn or Not to Learn Features for Deformable Registration?”, Deep Learning Fails (DLF) Workshop held in conjunction with 21st International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2018, Lecture Notes in Computer Science, Springer, LNCS 11037, pp. 119-129, 2018. (oral presentation) [preprint] [link] [presentation]

Peer reviewed Short Papers

  1. R. Mehta, Vitor Albiero, Li Chen, Ivan Evtimov, Tamar Glaser, Zhiheng Li, Tal Hassner, “You Only Need a Good Embeddings Extractor to Fix Spurious Correlations”, Responsible Computer Vision (RCV) Workshop, European Conference on Computer Vision (ECCV) 2022.

  2. R. Mehta, A. Filos, Y. Gal, T. Arbel, “Uncertainty Evaluation Metrics for Brain Tumour Segmentation”, Medical Imaging with Deep Learning (MIDL) 2020 [paper] [presentation] [video]

  3. R. Mehta, T. Arbel, “RS-Net: Regression-Segmentation 3D CNN for Synthesis of Full Resolution Missing Brain MRI in the Presence of Tumours”, Medical Imaging meets NeurIPS (Med-NeurIPS) 2018 workshop held in conjunction with 32nd Conference on Neural Information Processing Systems (NeurIPS) 2018. (acceptance rate ~23%) [paper] [poster]

ArXiv Pre-print

  1. J. Sivaswamy, A. Thottupattu*, Mythri V.*, R. Mehta, R. Sheelakumari, C. Keshavdas, “Sub-cortical structure segmentation databse for young population”, arXiv preprint arXiv:2111.01561, 2021, [preprint]

  2. S. Bakas, M. Reyes, …, T. Arbel, …, R. Mehta, …, B. Menze, “Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge”, arXiv preprint arXiv:1811.02629, 2018 [preprint]

Theses

  1. R. Mehta. “Population specific template construction and brain structure segmentation using deep learning methods.” Master's Thesis, International Institute of Information Techology - Hyderabad (IIIT-H), India, 2017. [link] [presentation]