1 citations · 2 across the 7 of their papers we have counts for
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…
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…
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…
AA-CLIP: Enhancing Zero-shot Anomaly Detection via Anomaly-Aware CLIP
Wenxin Ma, Xu Zhang, Qingsong Yao +6
Anomaly detection (AD) identifies outliers for applications like defect and lesion detection. While CLIP shows promise for zero-shot AD tasks due to its strong generalization capab…
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…
Composed Multi-modal Retrieval: A Survey of Approaches and Applications
Kun Zhang, Jingyu Li, Zhe Li +10
The burgeoning volume of multi-modal data necessitates advanced retrieval paradigms beyond unimodal and cross-modal approaches. Composed Multi-modal Retrieval (CMR) emerges as a pi…