3 citations · 3 across the 7 of their papers we have counts for
9 papers · 1 filter
LoMeVQA: A Comprehensive Benchmark for Longitudinal Medical VQA
Zhilin Wu, Zhangkai Ni, Chengmei Yang +4
In clinical practice, patients often undergo multiple imaging examinations over successive visits, yielding longitudinal data. Modeling such temporal information is crucial for rel…
M-IDoL: Information Decomposition for Modality-Specific and Diverse Representation Learning in Medical Foundation Model
Yihang Liu, Longzhen Yang, Jiaxiong Yang +3
Medical foundation models (MFMs) aim to learn universal representations from multimodal medical images that can generalize effectively to diverse downstream clinical tasks. However…
EntropyPrune: Matrix Entropy Guided Visual Token Pruning for Multimodal Large Language Models
Yahong Wang, Juncheng Wu, Zhangkai Ni +6
Multimodal large language models (MLLMs) incur substantial inference cost due to the processing of hundreds of visual tokens per image. Although token pruning has proven effective…
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…
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…
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…