activity
20172026
collaborators

13 papers

cs.LG2026

Probing, Fusion, and Trustworthiness: A Systematic Evaluation of Foundation Model Representations for Multimodal Cancer Analysis

Jingyu Hu, Giuseppe Tripodi, Reed Naidoo +2

Foundation models (FMs) have emerged as powerful representation extractors for medical data, yet their generalizability to datasets under distribution shift remains underexplored.…

eess.IV2026

AdaLoRA-QAT: Adaptive Low-Rank and Quantization-Aware Segmentation

Prantik Deb, Srimanth Dhondy, N. Ramakrishna +3

Chest X-ray (CXR) segmentation is an important step in computer-aided diagnosis, yet deploying large foundation models in clinical settings remains challenging due to computational…

eess.IV2025

Achieving Fair Skin Lesion Detection through Skin Tone Normalization and Channel Pruning

Zihan Wei, Tapabrata Chakraborti

Recent works have shown that deep learning based skin lesion image classification models trained on unbalanced dataset can exhibit bias toward protected demographic attributes such…

cs.CV2025

Ambiguous Medical Image Segmentation Using Diffusion Schrödinger Bridge

Lalith Bharadwaj Baru, Kamalaker Dadi, Tapabrata Chakraborti +1

Accurate segmentation of medical images is challenging due to unclear lesion boundaries and mask variability. We introduce \emph{Segmentation Schödinger Bridge (SSB)}, the first ap…

cs.LG2025

Conformal uncertainty quantification to evaluate predictive fairness of foundation AI model for skin lesion classes across patient demographics

Swarnava Bhattacharyya, Umapada Pal, Tapabrata Chakraborti

Deep learning based diagnostic AI systems based on medical images are starting to provide similar performance as human experts. However these data hungry complex systems are inhere…

cs.LG2025

VECT-GAN: A variationally encoded generative model for overcoming data scarcity in pharmaceutical science

Youssef Abdalla, Marrisa Taub, Eleanor Hilton +7

Data scarcity in pharmaceutical research has led to reliance on labour-intensive trial-and-error approaches for development rather than data-driven methods. While Machine Learning…