Jimut Bahan Pal

About

I am a 4th year Ph.D. Student in Centre for Machine Intelligence and Data Science (C-MInDS) at IIT Bombay and advised by Professor Suyash P. Awate. I have completed my postgraduation from Ramakrishna Mission Vivekananda Educational and Research Institute where I pursued M. Sc. in Computer Science. I have completed my undergraduate studies at St. Xavier's College, Kolkata.

My research is situated at the intersection of Deep Learning, Computer Vision, and Medicine, with a primary goal of developing robust, interpretable, and computationally efficient algorithms for medical image analysis. A central theme of my work is medical image segmentation. I design and implement novel supervised learning methodologies that leverage statistical analysis and incorporate mathematical priors. This approach ensures that my models are resilient to common clinical challenges, including:
* Data Scarcity: Performing effectively with limited annotated datasets.
* Computational Constraints: Operating efficiently in low-resource environments.
* Data Heterogeneity: Maintaining performance across diverse imaging modalities and handling outlier data.

Currently, I am expanding this focus to include multimodal learning. My latest research investigates the synergy between visual data and Natural Language Processing (NLP) of clinical reports, aiming to build integrated diagnostic systems that offer superior accuracy and deeper clinical insights.

Employment

• Indian Institute of Technology Bombay Research Fellow, PhD@AI
  2022 - 2027

Education

• Indian Institute of Technology Bombay Research Fellow, PhD@AI
  2022 - 2028
• Saint Xavier's College B.Sc.
  2016 - 2019
• Central Modern School, Baranagar ISC
  2008 - 2016
• St Agnes Branch School, IIT KGP
  2001 - 2008

Projects & Funding

Projects & funding information is unavailable.

Publications (11)

• Learning a Sampling-Free Variational DNN Plugin from Tiny Training Sets to Refine OOD Segmentation With Uncertainty Estimation
  Machine Learning for Biomedical Imaging 2026 DOI: 10.59275/j.melba.2026-6d54
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• Reviving Poor Object Segmentations in OOD Medical Images using Variational-Deep-PCA Modeling on Segmentation Maps with Sampling-Free Learning
  2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2025 DOI: 10.1109/wacv61041.2025.00907
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• A HARD CONVEX-SHAPE CONSTRAINT IN DNNS FOR OBJECT SEGMENTATION
  Proceedings - International Conference on Image Processing, ICIP 2024 DOI: 10.1109/ICIP51287.2024.10648240
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• Advancing instance segmentation and WBC classification in peripheral blood smear through domain adaptation: A study on PBC and the novel RV-PBS datasets
  Expert Systems with Applications 2024 DOI: 10.1016/j.eswa.2024.123660
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• Convex Segments for Convex Objects Using DNN Boundary Tracing and Graduated Optimization
  Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2024 DOI: 10.1007/978-3-031-72111-3_9
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• QUBIQ: Uncertainty Quantification for Biomedical Image Segmentation Challenge
  arXiv 2024 DOI: 10.48550/arXiv.2405.18435
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• Improving multi-scale attention networks: Bayesian optimization for segmenting medical images
  Imaging Science Journal 2023 DOI: 10.1080/13682199.2023.2174657
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• Biomedical image analysis competitions: The state of current participation practice
  arXiv 2022 DOI: 10.48550/arXiv.2212.08568
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• Holistic Network for Quantifying Uncertainties in Medical Images
  Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries 2022 DOI: 10.1007/978-3-031-09002-8_49
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• Classifying Chest X-Ray COVID-19 images via Transfer Learning
  2021 Ethics and Explainability for Responsible Data Science (EE-RDS) 2021 DOI: 10.1109/ee-rds53766.2021.9708580
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