215 citations · 236 across the 21 of their papers we have counts for
23 papers
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…
Policy-Driven CT-Agent: Modeling Phase-Aware Diagnostic Control for Clinically Consistent CT Reasoning
Yanmeng Dong, Han Li, Yujia Li +7
Computed Tomography (CT) diagnosis often relies on dynamic selection of imaging phases, such as non-contrast, arterial, or venous phases, based on preliminary findings, clinical su…
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…
Thinking Like a Clinician: A Cognitive AI Agent for Clinical Diagnosis via Panoramic Profiling and Adversarial Debate
Zhiqi Lv, Duofan Tu, Jun Li +4
The application of large language models (LLMs) in clinical decision support faces significant challenges of "tunnel vision" and diagnostic hallucinations present in their processi…
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…
MACD: Multi-Agent Clinical Diagnosis with Self-Learned Knowledge for LLM
Wenliang Li, Rui Yan, Xu Zhang +10
Large language models (LLMs) have shown promise in supporting medical diagnosis, with prompting-based methods offering a flexible and deployable means of capability enhancement. Ho…
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…
From Pretraining to Privacy: Federated Ultrasound Foundation Model with Self-Supervised Learning
Yuncheng Jiang, Chun-Mei Feng, Jinke Ren +15
Ultrasound imaging is widely used in clinical diagnosis due to its non-invasive nature and real-time capabilities. However, traditional ultrasound diagnostics relies heavily on phy…
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…
Knowledge Matters: Radiology Report Generation with General and Specific Knowledge
Shuxin Yang, Xian Wu, Shen Ge +2
Automatic radiology report generation is critical in clinics which can relieve experienced radiologists from the heavy workload and remind inexperienced radiologists of misdiagnosi…
Artifact Disentanglement Network for Unsupervised Metal Artifact Reduction
Haofu Liao, Wei-An Lin, Jianbo Yuan +2
Current deep neural network based approaches to computed tomography (CT) metal artifact reduction (MAR) are supervised methods which rely heavily on synthesized data for training.…