2 citations · 2 across the 6 of their papers we have counts for
6 papers
Do Medical Vision Language Models Actually See? A Counterfactual Grounding Framework and Hard-Negative Contrastive Training for Visually-Reliant Medical VLMs
Anas Zafar, Leema Krishna Murali, Siddhant Bharadwaj +2
Large vision language models (VLMs) report strong accuracy on medical question-answering, yet it remains unclear whether they reason from visual evidence or exploit textual shortcu…
Medmarks: A Comprehensive Open-Source LLM Benchmark Suite for Medical Tasks
Benjamin Warner, Ratna Sagari Grandhi, Max Kieffer +32
Evaluating large language models (LLMs) for medical applications remains challenging due to benchmark saturation, limited data accessibility, and insufficient coverage of relevant…
Spatial Reasoning is Not a Free Lunch: A Controlled Study on LLaVA
Nahid Alam, Leema Krishna Murali, Siddhant Bharadwaj +7
Vision-language models (VLMs) have advanced rapidly, yet they still struggle with basic spatial reasoning. Despite strong performance on general benchmarks, modern VLMs remain brit…
Beyond Accuracy: Evaluating Visual Grounding In Multimodal Medical Reasoning
Anas Zafar, Leema Krishna Murali, Ashish Vashist
Recent work shows that text-only reinforcement learning with verifiable rewards (RLVR) can match or outperform image-text RLVR on multimodal medical VQA benchmarks, suggesting curr…
The Spatial Blindspot of Vision-Language Models
Nahid Alam, Leema Krishna Murali, Siddhant Bharadwaj +7
Vision-language models (VLMs) have advanced rapidly, but their ability to capture spatial relationships remains a blindspot. Current VLMs are typically built with contrastive langu…
Histo-fetch -- On-the-fly processing of gigapixel whole slide images simplifies and speeds neural network training
Brendon Lutnick, Leema Krishna Murali, Brandon Ginley +2
We created a custom pipeline (histo-fetch) to efficiently extract random patches and labels from pathology whole slide images (WSIs) for input to a neural network on-the-fly. We pr…