107 citations · 179 across the 12 of their papers we have counts for
13 papers
OpenTumorBoard: A Real-World Benchmark of Multidisciplinary Tumor Board Discussion Trajectories
Anqi Li, Zhixuan Ge, Yixuan Duan +9
Multidisciplinary tumor boards integrate multimodal clinical observations and longitudinal patient histories through specialist discussions, yet benchmarks rarely capture these rea…
Volumetric Radiology AI in the Era of Multimodal Large Language Models
Zanting Ye, Shengyuan Liu, Xin Liu +16
Advances in multimodal large language models (MLLMs) are extending radiological artificial intelligence (AI) beyond task-specific image analysis toward multimodal understanding and…
Foundation VAEs for 3D CT Reconstruction, Augmentation, and Generation
Qi Chen, Shuhan Ding, Yu Gu +5
Variational autoencoders (VAEs) compress high resolution CT volumes into compact latents while preserving clinically relevant structure. However, training CT-specific VAEs from scr…
AURAD: Anatomy-Pathology Unified Radiology Synthesis with Progressive Representations
Shuhan Ding, Jingjing Fu, Yu Gu +6
Medical image synthesis has become an essential strategy for augmenting datasets and improving model generalization in data-scarce clinical settings. However, fine-grained and cont…
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…
X-Reasoner: Towards Generalizable Reasoning Across Modalities and Domains
Qianchu Liu, Sheng Zhang, Guanghui Qin +9
Recent proprietary models (e.g., o3) have begun to demonstrate strong multimodal reasoning capabilities. Yet, most existing open-source research concentrates on training text-only…
Magma: A Foundation Model for Multimodal AI Agents
Jianwei Yang, Reuben Tan, Qianhui Wu +10
We present Magma, a foundation model that serves multimodal AI agentic tasks in both the digital and physical worlds. Magma is a significant extension of vision-language (VL) model…
Universal Abstraction: Harnessing Frontier Models to Structure Real-World Data at Scale
Cliff Wong, Sam Preston, Qianchu Liu +22
A significant fraction of real-world patient information resides in unstructured clinical text. Medical abstraction extracts and normalizes key structured attributes from free-text…
MedImageInsight: An Open-Source Embedding Model for General Domain Medical Imaging
Noel C. F. Codella, Ying Jin, Shrey Jain +28
In this work, we present MedImageInsight, an open-source medical imaging embedding model. MedImageInsight is trained on medical images with associated text and labels across a dive…
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…
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…
Foundation Models for Biomedical Image Segmentation: A Survey
Ho Hin Lee, Yu Gu, Theodore Zhao +9
Recent advancements in biomedical image analysis have been significantly driven by the Segment Anything Model (SAM). This transformative technology, originally developed for genera…
BiomedJourney: Counterfactual Biomedical Image Generation by Instruction-Learning from Multimodal Patient Journeys
Yu Gu, Jianwei Yang, Naoto Usuyama +5
Rapid progress has been made in instruction-learning for image editing with natural-language instruction, as exemplified by InstructPix2Pix. In biomedicine, such methods can be app…