3 citations · 4 across the 7 of their papers we have counts for
7 papers
Reinforcement Learning for Evidence-Seeking Diagnostic Reasoning with Large Language Models
Shengyi Hua, Kangzhe Hu, Conghui He +2
Recent reasoning-centric Large Language Models (LLMs) have made significant strides, yet they predominantly operate on a passive-inference pattern that assumes complete information…
PathFound: An Agentic Multimodal Model Activating Evidence-seeking Pathological Diagnosis
Shengyi Hua, Jianfeng Wu, Tianle Shen +7
Recent pathological foundation models have substantially advanced visual representation learning and multimodal interaction. However, most models still rely on a static inference p…
PathOrchestra: A Comprehensive Foundation Model for Computational Pathology with Over 100 Diverse Clinical-Grade Tasks
Fang Yan, Jianfeng Wu, Jiawen Li +24
The complexity and variability inherent in high-resolution pathological images present significant challenges in computational pathology. While pathology foundation models leveragi…
O1 Replication Journey -- Part 3: Inference-time Scaling for Medical Reasoning
Zhongzhen Huang, Gui Geng, Shengyi Hua +5
Building upon our previous investigations of O1 replication (Part 1: Journey Learning [Qin et al., 2024] and Part 2: Distillation [Huang et al., 2024]), this work explores the pote…
DeReStainer: H&E to IHC Pathological Image Translation via Decoupled Staining Channels
Linda Wei, Shengyi Hua, Shaoting Zhang +1
Breast cancer is a highly fatal disease among cancers in women, and early detection is crucial for treatment. HER2 status, a valuable diagnostic marker based on Immunohistochemistr…
PathoTune: Adapting Visual Foundation Model to Pathological Specialists
Jiaxuan Lu, Fang Yan, Xiaofan Zhang +2
As natural image understanding moves towards the pretrain-finetune era, research in pathology imaging is concurrently evolving. Despite the predominant focus on pretraining patholo…
PathoDuet: Foundation Models for Pathological Slide Analysis of H&E and IHC Stains
Shengyi Hua, Fang Yan, Tianle Shen +2
Large amounts of digitized histopathological data display a promising future for developing pathological foundation models via self-supervised learning methods. Foundation models p…