8 citations · 13 across the 11 of their papers we have counts for
12 papers
Cross-Modal Clinical Knowledge Integration for Mammography Report Generation
Jiayi Zhu, Fuxiang Huang, Yu Xie +7
Breast cancer is a major global health concern, and mammography screening plays a central role in early detection. The large volume of screening examinations creates a substantial…
A Clinically Validated Foundation Model for Comprehensive Lung Pathology Interpretation
Zhengrui Guo, Zhengyu Zhang, Jiabo Ma +23
Pathological assessment guides lung cancer diagnosis, treatment selection, and prognostic evaluation, yet current CPath approaches rely on task-specific models for isolated objecti…
A Versatile Foundation Model for AI-enabled Mammogram Interpretation
Fuxiang Huang, Jiayi Zhu, Yunfang Yu +20
Breast cancer is the most commonly diagnosed cancer and the leading cause of cancer-related mortality in women globally. Mammography is essential for the early detection and diagno…
Solutions for Mitotic Figure Detection and Atypical Classification in MIDOG 2025
Shuting Xu, Runtong Liu, Zhixuan Chen +2
Deep learning has driven significant advances in mitotic figure analysis within computational pathology. In this paper, we present our approach to the Mitosis Domain Generalization…
A Versatile Pathology Co-pilot via Reasoning Enhanced Multimodal Large Language Model
Zhe Xu, Ziyi Liu, Junlin Hou +13
Multimodal large language models (MLLMs) have emerged as powerful tools for computational pathology, offering unprecedented opportunities to integrate pathological images with lang…
Segment Anything in Pathology Images with Natural Language
Zhixuan Chen, Junlin Hou, Liqi Lin +6
Pathology image segmentation is crucial in computational pathology for analyzing histological features relevant to cancer diagnosis and prognosis. However, current methods face maj…
An Explainable Biomedical Foundation Model via Large-Scale Concept-Enhanced Vision-Language Pre-training
Yuxiang Nie, Sunan He, Yequan Bie +14
The clinical adoption of artificial intelligence (AI) in medical imaging requires models that are both diagnostically accurate and interpretable to clinicians. While current multim…
Large Language Model with Region-guided Referring and Grounding for CT Report Generation
Zhixuan Chen, Yequan Bie, Haibo Jin +1
Computed tomography (CT) report generation is crucial to assist radiologists in interpreting CT volumes, which can be time-consuming and labor-intensive. Existing methods primarily…
GSCo: Towards Generalizable AI in Medicine via Generalist-Specialist Collaboration
Sunan He, Yuxiang Nie, Hongmei Wang +21
Generalist foundation models (GFMs) are renowned for their exceptional capability and flexibility in effectively generalizing across diverse tasks and modalities. In the field of m…
Dia-LLaMA: Towards Large Language Model-driven CT Report Generation
Zhixuan Chen, Luyang Luo, Yequan Bie +1
Medical report generation has achieved remarkable advancements yet has still been faced with several challenges. First, the inherent imbalance in the distribution of normal and abn…
XCoOp: Explainable Prompt Learning for Computer-Aided Diagnosis via Concept-guided Context Optimization
Yequan Bie, Luyang Luo, Zhixuan Chen +1
Utilizing potent representations of the large vision-language models (VLMs) to accomplish various downstream tasks has attracted increasing attention. Within this research field, s…
Iterative Semi-Supervised Learning for Abdominal Organs and Tumor Segmentation
Jiaxin Zhuang, Luyang Luo, Zhixuan Chen +1
Deep-learning (DL) based methods are playing an important role in the task of abdominal organs and tumors segmentation in CT scans. However, the large requirements of annotated dat…