3 citations · 7 across the 19 of their papers we have counts for
19 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…
Evading Visual Aphasia: Contrastive Adaptive Semantic Token Pruning for Vision-Language Models
Jie Ma, Yihang Liu, Zhike Qiu +2
Are low-attention visual tokens truly redundant in vision-language reasoning? Existing pruning methods often assume so, ranking visual tokens by shallow text-to-image attention and…
AR2-4FV: Anchored Referring and Re-identification for Long-Term Grounding in Fixed-View Videos
Teng Yan, Yihan Liu, Jiongxu Chen +3
Long-term language-guided referring in fixed-view videos is challenging: the referent may be occluded or leave the scene for long intervals and later re-enter, while framewise refe…
OMG-Agent: Toward Robust Missing Modality Generation with Decoupled Coarse-to-Fine Agentic Workflows
Ruiting Dai, Zheyu Wang, Haoyu Yang +6
Data incompleteness severely impedes the reliability of multimodal systems. Existing reconstruction methods face distinct bottlenecks: conventional parametric/generative models are…
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…
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…
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…
3D-GRES: Generalized 3D Referring Expression Segmentation
Changli Wu, Yihang Liu, Jiayi Ji +6
3D Referring Expression Segmentation (3D-RES) is dedicated to segmenting a specific instance within a 3D space based on a natural language description. However, current approaches…
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…