3 citations · 7 across the 12 of their papers we have counts for
12 papers
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…
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…
From Documents to Spans: Span-Centric Learning for LLM-based Explainable Medical Coding
Xu Zhang, Wenxin Ma, Chenxu Wu +5
Medical coding, which assigns standardized ICD codes to clinical documents, is a fundamental yet challenging task in modern healthcare. It requires extensive knowledge of a large c…
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…
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…
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…
E3D-GPT: Enhanced 3D Visual Foundation for Medical Vision-Language Model
Haoran Lai, Zihang Jiang, Qingsong Yao +6
The development of 3D medical vision-language models holds significant potential for disease diagnosis and patient treatment. However, compared to 2D medical images, 3D medical ima…
CARZero: Cross-Attention Alignment for Radiology Zero-Shot Classification
Haoran Lai, Qingsong Yao, Zihang Jiang +4
The advancement of Zero-Shot Learning in the medical domain has been driven forward by using pre-trained models on large-scale image-text pairs, focusing on image-text alignment. H…
ECAMP: Entity-centered Context-aware Medical Vision Language Pre-training
Rongsheng Wang, Qingsong Yao, Zihang Jiang +4
Despite significant advancements in medical vision-language pre-training, existing methods have largely overlooked the inherent linguistic complexity and imbalanced isssue within m…
Structure-Guided Self-Supervised Matching for One-Shot Medical Landmark Detection
Qingsong Yao, Zhen Huang, Ao Wang +4
Medical landmark detection usually requires accurate expert annotations, which are laborious and difficult to scale across anatomical regions. In this work, we study an extreme ann…