2 citations · 7 across the 13 of their papers we have counts for
6 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…
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…
A self-supervised framework for learning whole slide representations
Xinhai Hou, Cheng Jiang, Akhil Kondepudi +4
Whole slide imaging is fundamental to biomedical microscopy and computational pathology. Previously, learning representations for gigapixel-sized whole slide images (WSIs) has reli…