157 citations · 161 across the 11 of their papers we have counts for
13 papers
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.…
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…
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…
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…
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…
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…
Learning from data with structured missingness
Robin Mitra, Sarah F. McGough, Tapabrata Chakraborti +17
Missing data are an unavoidable complication in many machine learning tasks. When data are `missing at random' there exist a range of tools and techniques to deal with the issue. H…
Microscopic fine-grained instance classification through deep attention
Mengran Fan, Tapabrata Chakrabort, Eric I-Chao Chang +2
Fine-grained classification of microscopic image data with limited samples is an open problem in computer vision and biomedical imaging. Deep learning based vision systems mostly d…
Contrastive Fairness in Machine Learning
Tapabrata Chakraborti, Arijit Patra, Alison Noble
Was it fair that Harry was hired but not Barry? Was it fair that Pam was fired instead of Sam? How can one ensure fairness when an intelligent algorithm takes these decisions inste…
Distance Metric Learned Collaborative Representation Classifier
Tapabrata Chakraborti, Brendan McCane, Steven Mills +1
Any generic deep machine learning algorithm is essentially a function fitting exercise, where the network tunes its weights and parameters to learn discriminatory features by minim…
PProCRC: Probabilistic Collaboration of Image Patches
Tapabrata Chakraborti, Brendan McCane, Steven Mills +1
We present a conditional probabilistic framework for collaborative representation of image patches. It incorporates background compensation and outlier patch suppression into the m…
CoCoNet: A Collaborative Convolutional Network
Tapabrata Chakraborti, Brendan McCane, Steven Mills +1
We present an end-to-end deep network for fine-grained visual categorization called Collaborative Convolutional Network (CoCoNet). The network uses a collaborative layer after the…
LOOP Descriptor: Local Optimal Oriented Pattern
Tapabrata Chakraborti, Brendan McCane, Steven Mills +1
This letter introduces the LOOP binary descriptor (local optimal oriented pattern) that encodes rotation invariance into the main formulation itself. This makes any post processing…