199 citations · 437 across the 13 of their papers we have counts for
13 papers
ReXVQA: A Large-scale Visual Question Answering Benchmark for Generalist Chest X-ray Understanding
Ankit Pal, Jung-Oh Lee, Xiaoman Zhang +5
We present ReXVQA, the largest and most comprehensive benchmark for visual question answering (VQA) in chest radiology, comprising approximately 696,000 questions paired with 160,0…
Gemini Goes to Med School: Exploring the Capabilities of Multimodal Large Language Models on Medical Challenge Problems & Hallucinations
Ankit Pal, Malaikannan Sankarasubbu
Large language models have the potential to be valuable in the healthcare industry, but it's crucial to verify their safety and effectiveness through rigorous evaluation. For this…
Med-HALT: Medical Domain Hallucination Test for Large Language Models
Ankit Pal, Logesh Kumar Umapathi, Malaikannan Sankarasubbu
This research paper focuses on the challenges posed by hallucinations in large language models (LLMs), particularly in the context of the medical domain. Hallucination, wherein the…
Federated Learning for Healthcare Domain - Pipeline, Applications and Challenges
Madhura Joshi, Ankit Pal, Malaikannan Sankarasubbu
Federated learning is the process of developing machine learning models over datasets distributed across data centers such as hospitals, clinical research labs, and mobile devices…
MedMCQA : A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering
Ankit Pal, Logesh Kumar Umapathi, Malaikannan Sankarasubbu
This paper introduces MedMCQA, a new large-scale, Multiple-Choice Question Answering (MCQA) dataset designed to address real-world medical entrance exam questions. More than 194k h…
Bayesian Optimization of Bose-Einstein Condensates
Tamil Arasan Bakthavatchalam, Suriyadeepan Ramamoorthy, Malaikannan Sankarasubbu +2
Machine Learning methods are emerging as faster and efficient alternatives to numerical simulation techniques. The field of Scientific Computing has started adopting these data-dri…
Small-Bench NLP: Benchmark for small single GPU trained models in Natural Language Processing
Kamal Raj Kanakarajan, Bhuvana Kundumani, Malaikannan Sankarasubbu
Recent progress in the Natural Language Processing domain has given us several State-of-the-Art (SOTA) pretrained models which can be finetuned for specific tasks. These large mode…
Pay Attention to the cough: Early Diagnosis of COVID-19 using Interpretable Symptoms Embeddings with Cough Sound Signal Processing
Ankit Pal, Malaikannan Sankarasubbu
COVID-19 (coronavirus disease 2019) pandemic caused by SARS-CoV-2 has led to a treacherous and devastating catastrophe for humanity. At the time of writing, no specific antivirus d…
Multi-Label Text Classification using Attention-based Graph Neural Network
Ankit Pal, Muru Selvakumar, Malaikannan Sankarasubbu
In Multi-Label Text Classification (MLTC), one sample can belong to more than one class. It is observed that most MLTC tasks, there are dependencies or correlations among labels. E…
Detecting Parking Spaces in a Parcel using Satellite Images
Murugesan Vadivel, SelvaKumar Murugan, Suriyadeepan Ramamoorthy +2
Remote Sensing Images from satellites have been used in various domains for detecting and understanding structures on the ground surface. In this work, satellite images were used f…
Compositional Attention Networks for Interpretability in Natural Language Question Answering
Muru Selvakumar, Suriyadeepan Ramamoorthy, Vaidheeswaran Archana +1
MAC Net is a compositional attention network designed for Visual Question Answering. We propose a modified MAC net architecture for Natural Language Question Answering. Question An…
PHI Scrubber: A Deep Learning Approach
Abhai Kollara Dilip, Kamal Raj K, Malaikannan Sankarasubbu
Confidentiality of patient information is an essential part of Electronic Health Record System. Patient information, if exposed, can cause a serious damage to the privacy of indivi…
Convolutional Neural Networks In Classifying Cancer Through DNA Methylation
Soham Chatterjee, Archana Iyer, Satya Avva +2
DNA Methylation has been the most extensively studied epigenetic mark. Usually a change in the genotype, DNA sequence, leads to a change in the phenotype, observable characteristic…