5 citations · 11 across the 7 of their papers we have counts for
7 papers
Framework for Co-distillation Driven Federated Learning to Address Class Imbalance in Healthcare
Suraj Racha, Shubh Gupta, Humaira Firdowse +3
Federated Learning (FL) is a pioneering approach in distributed machine learning, enabling collaborative model training across multiple clients while retaining data privacy. Howeve…
Taming the Tail: Leveraging Asymmetric Loss and Pade Approximation to Overcome Medical Image Long-Tailed Class Imbalance
Pankhi Kashyap, Pavni Tandon, Sunny Gupta +3
Long-tailed problems in healthcare emerge from data imbalance due to variability in the prevalence and representation of different medical conditions, warranting the requirement of…
Exploring Gradient Subspaces: Addressing and Overcoming LoRA's Limitations in Federated Fine-Tuning of Large Language Models
Navyansh Mahla, Kshitij Sharad Jadhav, Ganesh Ramakrishnan
Large Language Models (LLMs) have demonstrated remarkable capabilities across various domains, particularly in task generalization for both text and vision data. While fine-tuning…
MedSAGa: Few-shot Memory Efficient Medical Image Segmentation using Gradient Low-Rank Projection in SAM
Navyansh Mahla, Annie D'souza, Shubh Gupta +2
The application of large-scale models in medical image segmentation demands substantial quantities of meticulously annotated data curated by experts along with high computational r…
Few shot chain-of-thought driven reasoning to prompt LLMs for open ended medical question answering
Saeel Sandeep Nachane, Ojas Gramopadhye, Prateek Chanda +5
In this paper, we propose a modified version of the MedQA-USMLE dataset, named MEDQA-OPEN, which contains open-ended medical questions without options to mimic clinical scenarios,…
FESS Loss: Feature-Enhanced Spatial Segmentation Loss for Optimizing Medical Image Analysis
Charulkumar Chodvadiya, Navyansh Mahla, Kinshuk Gaurav Singh +1
Medical image segmentation is a critical process in the field of medical imaging, playing a pivotal role in diagnosis, treatment, and research. It involves partitioning of an image…
INSITE: labelling medical images using submodular functions and semi-supervised data programming
Akshat Gautam, Anurag Shandilya, Akshit Srivastava +3
The necessity of large amounts of labeled data to train deep models, especially in medical imaging creates an implementation bottleneck in resource-constrained settings. In Insite…