3 citations · 5 across the 12 of their papers we have counts for
12 papers
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…
Visual Neural Decoding via Improved Visual-EEG Semantic Consistency
Hongzhou Chen, Lianghua He, Yihang Liu +3
Visual neural decoding aims to extract and interpret original visual experiences directly from human brain activity. Recent studies have demonstrated the feasibility of decoding vi…
FeaInfNet: Diagnosis in Medical Image with Feature-Driven Inference and Visual Explanations
Yitao Peng, Lianghua He, Die Hu +3
Interpretable deep learning models have received widespread attention in the field of image recognition. Due to the unique multi-instance learning of medical images and the difficu…
Hierarchical Dynamic Masks for Visual Explanation of Neural Networks
Yitao Peng, Longzhen Yang, Yihang Liu +1
Saliency methods generating visual explanatory maps representing the importance of image pixels for model classification is a popular technique for explaining neural network decisi…
Decoupling Deep Learning for Interpretable Image Recognition
Yitao Peng, Yihang Liu, Longzhen Yang +1
The interpretability of neural networks has recently received extensive attention. Previous prototype-based explainable networks involved prototype activation in both reasoning and…
MDM: Multiple Dynamic Masks for Visual Explanation of Neural Networks
Yitao Peng, Longzhen Yang, Yihang Liu +1
The Class Activation Map (CAM) lookup of a neural network tells us to which regions the neural network focuses when it makes a decision. In the past, the CAM search method was depe…
Variational Transformer: A Framework Beyond the Trade-off between Accuracy and Diversity for Image Captioning
Longzhen Yang, Yihang Liu, Yitao Peng +1
Accuracy and Diversity are two essential metrizable manifestations in generating natural and semantically correct captions. Many efforts have been made to enhance one of them with…