182 citations · 243 across the 15 of their papers we have counts for
7 papers · 1 filter
Learning complete and explainable visual representations from itemized text supervision
Yiwei Lyu, Chenhui Zhao, Soumyanil Banerjee +5
Training vision models with language supervision enables general and transferable representations. However, many visual domains, especially non-object-centric domains such as medic…
Health system learning achieves generalist neuroimaging models
Akhil Kondepudi, Akshay Rao, Chenhui Zhao +14
Frontier artificial intelligence (AI) models, such as OpenAI's GPT-5 and Meta's DINOv3, have advanced rapidly through training on internet-scale public data, yet such systems lack…
CodeV: Code with Images for Faithful Visual Reasoning via Tool-Aware Policy Optimization
Xinhai Hou, Shaoyuan Xu, Manan Biyani +4
Agentic vision-language models are increasingly trained to "think with images" by calling image operations. However, we show that high final-answer accuracy often hides unfaithful…
Learning neuroimaging models from health system-scale data
Yiwei Lyu, Samir Harake, Asadur Chowdury +18
Neuroimaging is a ubiquitous tool for evaluating patients with neurological diseases. The global demand for magnetic resonance imaging (MRI) studies has risen steadily, placing sig…
Intelligent Histology for Tumor Neurosurgery
Xinhai Hou, Akhil Kondepudi, Cheng Jiang +27
The importance of rapid and accurate histologic analysis of surgical tissue in the operating room has been recognized for over a century. Our standard-of-care intraoperative pathol…
Towards Scalable Language-Image Pre-training for 3D Medical Imaging
Chenhui Zhao, Yiwei Lyu, Asadur Chowdury +6
The scalability of current language-image pre-training for 3D medical imaging, such as CT and MRI, is constrained by the need for radiologists to manually curate raw clinical studi…
CNS-Obsidian: A Neurosurgical Vision-Language Model Built From Scientific Publications
Anton Alyakin, Jaden Stryker, Daniel Alexander Alber +29
General-purpose VLMs demonstrate impressive capabilities, but their opaque training on uncurated internet data poses critical limitations for high-stakes decision-making, such as i…