3 citations · 7 across the 19 of their papers we have counts for
6 papers · 1 filter
When Token Pruning is Worse than Random: Understanding Visual Token Information in VLLMs
Yahong Wang, Juncheng Wu, Zhangkai Ni +8
Vision Large Language Models (VLLMs) incur high computational costs due to their reliance on hundreds of visual tokens to represent images. While token pruning offers a promising s…
Pathology-CoT: Learning Visual Chain-of-Thought Agent from Expert Whole Slide Image Diagnosis Behavior
Sheng Wang, Ruiming Wu, Charles Herndon +4
Diagnosing a whole-slide image is an interactive, multi-stage process of changing magnification and moving between fields. Although recent pathology foundation models demonstrated…
Self-Supervised Anatomical Consistency Learning for Vision-Grounded Medical Report Generation
Longzhen Yang, Zhangkai Ni, Ying Wen +3
Vision-grounded medical report generation aims to produce clinically accurate descriptions of medical images, anchored in explicit visual evidence to improve interpretability and f…
A co-evolving agentic AI system for medical imaging analysis
Songhao Li, Jonathan Xu, Tiancheng Bao +11
Agentic AI is rapidly advancing in healthcare and biomedical research. However, in medical image analysis, their performance and adoption remain limited due to the lack of a robust…
AFiRe: Anatomy-Driven Self-Supervised Learning for Fine-Grained Representation in Radiographic Images
Yihang Liu, Lianghua He, Ying Wen +2
Current self-supervised methods, such as contrastive learning, predominantly focus on global discrimination, neglecting the critical fine-grained anatomical details required for ac…
ClinKD: Cross-Modal Clinical Knowledge Distiller For Multi-Task Medical Images
Hongyu Ge, Longkun Hao, Zihui Xu +5
Medical Visual Question Answering (Med-VQA) represents a critical and challenging subtask within the general VQA domain. Despite significant advancements in general VQA, multimodal…