activity
20232026
most citedBiomedParse: a biomedical foundation model for image parsing of everything everywhere all at once

107 citations · 253 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CV2026

GigaPath-Flash and GigaTIME-Flash: Efficient Pathology Foundation Models for Whole-Slide and Tumor Microenvironment Analysis

Naoto Usuyama, Jeya Maria Jose Valanarasu, Sicong Yao +27

Foundation models have emerged as a driving force in computational pathology, with the potential to transform cancer diagnosis, prognosis, and treatment selection by learning trans…

cs.AI2025★ 1 cited

The Illusion of Readiness in Health AI

Yu Gu, Jingjing Fu, Xiaodong Liu +29

Large language models have demonstrated remarkable performance in a wide range of medical benchmarks. Yet underneath the seemingly promising results lie salient growth areas, espec…

cs.CV2024★ 107 cited

BiomedParse: a biomedical foundation model for image parsing of everything everywhere all at once

Theodore Zhao, Yu Gu, Jianwei Yang +12

Biomedical image analysis is fundamental for biomedical discovery in cell biology, pathology, radiology, and many other biomedical domains. Holistic image analysis comprises interd…

cs.CL2024★ 40 cited

Towards a clinically accessible radiology foundation model: open-access and lightweight, with automated evaluation

Juan Manuel Zambrano Chaves, Shih-Cheng Huang, Yanbo Xu +24

The scaling laws and extraordinary performance of large foundation models motivate the development and utilization of such models in biomedicine. However, despite early promising r…

cs.CL2024★ 2 cited

Attribute Structuring Improves LLM-Based Evaluation of Clinical Text Summaries

Zelalem Gero, Chandan Singh, Yiqing Xie +6

Summarizing clinical text is crucial in health decision-support and clinical research. Large language models (LLMs) have shown the potential to generate accurate clinical text summ…

cs.CL2023★ 1 cited

DocLens: Multi-aspect Fine-grained Evaluation for Medical Text Generation

Yiqing Xie, Sheng Zhang, Hao Cheng +6

Medical text generation aims to assist with administrative work and highlight salient information to support decision-making. To reflect the specific requirements of medical text,…

cs.CV2023★ 102 cited

BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs

Sheng Zhang, Yanbo Xu, Naoto Usuyama +21

Biomedical data is inherently multimodal, comprising physical measurements and natural language narratives. A generalist biomedical AI model needs to simultaneously process differe…