2 citations · 4 across the 10 of their papers we have counts for
9 papers · 1 filter
Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models
Xi Xiao, Xingjian Li, Yunbei Zhang +7
Visual prompt tuning has emerged as a parameter-efficient fine-tuning approach for adapting large-scale Vision Transformers (ViTs) to downstream tasks. As its learnable prompts are…
MedVIGIL: Evaluating Trustworthy Medical VLMs Under Broken Visual Evidence
Hanqi Jiang, Junhao Chen, Mingyu Kang +12
Medical vision--language models (VLMs) are usually evaluated on intact image--question pairs, but trustworthy clinical use requires a stronger property: a model must recognise when…
A World Model of Radiologist Reading for Medical Image Representation Learning
Yiwei Li, Zihao Wu, Huaqin Zhao +5
Radiologist eye-tracking data provide a rich record of how experts search, compare, and accumulate evidence during image reading; yet, existing methods exploit this signal only par…
Conditional Evidence Reconstruction and Decomposition for Interpretable Multimodal Diagnosis
Shaowen Wan, Yanjun Lv, Lu Zhang +5
Neurobiological and neurodegenerative diseases are inherently multifactorial, arising from coupled influences spanning genetic susceptibility, brain alterations, and environmental…
IMA-MoE: An Interpretable Modality-Aware Mixture-of-Experts Framework for Characterizing the Neurobiological Signatures of Binge Eating Disorder
Lin Zhao, Qiaohui Gao, Elizabeth Martin +5
Binge eating disorder (BED) is the most prevalent eating disorder. However, current diagnostic frameworks remain largely grounded in symptom-based criteria rather than underlying b…
Shifting Adaptation from Weight Space to Memory Space: A Memory-Augmented Agent for Medical Image Segmentation
Bowen Chen, Qiaohui Gao, Shaowen Wan +6
Medical image segmentation is fundamental to clinical workflows, yet models trained on a single dataset often fail to generalize across institutions, scanners, or patient populatio…
Thinking with Gaze: Sequential Eye-Tracking as Visual Reasoning Supervision for Medical VLMs
Yiwei Li, Yifan Zhou, Huaqin Zhao +6
Vision--language models (VLMs) process images as visual tokens, yet their intermediate reasoning is often carried out in text, which can be suboptimal for visually grounded radiolo…
Neural Functional Alignment Space: Brain-Referenced Representation of Artificial Neural Networks
Ruiyu Yan, Hanqi Jiang, Yi Pan +4
We propose the Neural Functional Alignment Space (NFAS), a brain-referenced representational framework for characterizing artificial neural networks on equal functional grounds. NF…
Describe Anything in Medical Images
Xi Xiao, Yunbei Zhang, Thanh-Huy Nguyen +10
Localized image captioning has made significant progress with models like the Describe Anything Model (DAM), which can generate detailed region-specific descriptions without explic…