215 citations · 236 across the 21 of their papers we have counts for
17 papers · 1 filter
PathPilot: An Adaptive Question-Conditioned Multi-Magnification Navigation Agent for Whole-Slide Image Analysis
Zhengyang Xu, Han Li, Jingsong Liu +5
The diagnostic capability of WSI agents relies on effective slide navigation to identify diagnostically relevant regions and efficiently integrate evidence across the slide. While…
ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training
Rongsheng Wang, Fenghe Tang, Zihang Jiang +10
Learning transferable and interpretable representations from medical volumetric scans remains challenging due to complex anatomical structures and weak, heterogeneous supervision p…
Concept-to-Pixel: Prompt-Free Universal Medical Image Segmentation
Haoyun Chen, Fenghe Tang, Wenxin Ma +1
Universal medical image segmentation seeks to use a single foundational model to handle diverse tasks across multiple imaging modalities. However, existing approaches often rely he…
DiffVP: Differential Visual Semantic Prompting for LLM-Based CT Report Generation
Yuhe Tian, Kun Zhang, Haoran Ma +4
While large language models (LLMs) have advanced CT report generation, existing methods typically encode 3D volumes holistically, failing to distinguish informative cues from redun…
MMNavAgent: Multi-Magnification WSI Navigation Agent for Clinically Consistent Whole-Slide Analysis
Zhengyang Xu, Han Li, Jingsong Liu +10
Recent AI navigation approaches aim to improve Whole-Slide Image (WSI) diagnosis by modeling spatial exploration and selecting diagnostically relevant regions, yet most operate at…
GreenRFM: Learning a resource-efficient radiology vision-language foundation model via supervision-centric pre-training
Yingtai Li, Shuai Ming, Qiuli Wang +13
Radiology foundation models (RFMs) have largely inherited the scale-first recipe of natural-image vision--language pre-training. This recipe is difficult to deploy in 3D radiology,…
Med3D-R1: Incentivizing Clinical Reasoning in 3D Medical Vision-Language Models for Abnormality Diagnosis
Haoran Lai, Zihang Jiang, Kun Zhang +6
Developing 3D vision-language models with robust clinical reasoning remains a challenge due to the inherent complexity of volumetric medical imaging, the tendency of models to over…
UCAD: Uncertainty-guided Contour-aware Displacement for semi-supervised medical image segmentation
Chengbo Ding, Fenghe Tang, Shaohua Kevin Zhou
Existing displacement strategies in semi-supervised segmentation only operate on rectangular regions, ignoring anatomical structures and resulting in boundary distortions and seman…
Fusion of Multi-scale Heterogeneous Pathology Foundation Models for Whole Slide Image Analysis
Zhidong Yang, Xiuhui Shi, Wei Ba +7
Whole slide image (WSI) analysis has emerged as an increasingly essential technique in computational pathology. Recent advances in the pathology foundation models (FMs) have demons…
Towards Cellular-Scale Interpretability in Pathology Foundation Models for Biomarker Assessment
Jingsong Liu, Han Li, Zhengyang Xu +19
Molecular biomarker testing in pathology is often costly and tissue-consuming, limiting scalable clinical deployment. Artificial intelligence applied to hematoxylin and eosin (HE)-…
MedReason-R1: Learning to Reason for CT Diagnosis with Reinforcement Learning and Local Zoom
Yifan Li, Fenghe Tang, Yingtai Li +1
General-purpose large Vision-Language Models (VLMs) demonstrate strong capabilities in generating detailed descriptions for natural images. However, their performance in the medica…
U-Bench: A Comprehensive Understanding of U-Net through 100-Variant Benchmarking
Fenghe Tang, Chengqi Dong, Wenxin Ma +7
Over the past decade, U-Net has been the dominant architecture in medical image segmentation, leading to the development of thousands of U-shaped variants. Despite its widespread a…
More performant and scalable: Rethinking contrastive vision-language pre-training of radiology in the LLM era
Yingtai Li, Haoran Lai, Xiaoqian Zhou +4
The emergence of Large Language Models (LLMs) presents unprecedented opportunities to revolutionize medical contrastive vision-language pre-training. In this paper, we show how LLM…
SimCroP: Radiograph Representation Learning with Similarity-driven Cross-granularity Pre-training
Rongsheng Wang, Fenghe Tang, Qingsong Yao +8
Medical vision-language pre-training shows great potential in learning representative features from massive paired radiographs and reports. However, in computed tomography (CT) sca…
Bridged Semantic Alignment for Zero-shot 3D Medical Image Diagnosis
Haoran Lai, Zihang Jiang, Qingsong Yao +6
3D medical images such as computed tomography are widely used in clinical practice, offering a great potential for automatic diagnosis. Supervised learning-based approaches have ac…
SRSNetwork: Siamese Reconstruction-Segmentation Networks based on Dynamic-Parameter Convolution
Bingkun Nian, Fenghe Tang, Jianrui Ding +4
Dynamic convolution demonstrates outstanding representation capabilities, which are crucial for natural image segmentation. However, it fails when applied to medical image segmenta…
UOD: Universal One-shot Detection of Anatomical Landmarks
Heqin Zhu, Quan Quan, Qingsong Yao +2
One-shot medical landmark detection gains much attention and achieves great success for its label-efficient training process. However, existing one-shot learning methods are highly…