7 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…
From Documents to Spans: Scalable Supervision for Evidence-Based ICD Coding with LLMs
Xu Zhang, Wenxin Ma, Chenxu Wu +5
International Classification of Diseases (ICD) coding assigns diagnosis codes to clinical documents and is essential for healthcare billing and clinical analysis. Reliable coding r…
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…
Histomorphology-Guided Prototypical Multi-Instance Learning for Breast Cancer WSI Classification
Baizhi Wang, Rui Yan, Wenxin Ma +6
Histomorphology is crucial in cancer diagnosis. However, existing whole slide image (WSI) classification methods struggle to effectively incorporate histomorphology information, li…
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…
A General Knowledge Injection Framework for ICD Coding
Xu Zhang, Kun Zhang, Wenxin Ma +4
ICD Coding aims to assign a wide range of medical codes to a medical text document, which is a popular and challenging task in the healthcare domain. To alleviate the problems of l…