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Can Cross-Layer Transcoders Replace Vision Transformer Activations? An Interpretable Perspective on Vision
Gerasimos Chatzoudis, Konstantinos D. Polyzos, Zhuowei Li +4
Understanding the internal activations of Vision Transformers (ViTs) is critical for building interpretable and trustworthy models. While Sparse Autoencoders (SAEs) have been used…
LUCID-SAE: Learning Unified Vision-Language Sparse Codes for Interpretable Concept Discovery
Difei Gu, Yunhe Gao, Gerasimos Chatzoudis +6
Sparse autoencoders (SAEs) offer a natural path toward comparable explanations across different representation spaces. However, current SAEs are trained per modality, producing dic…
Anatomy-VLM: A Fine-grained Vision-Language Model for Medical Interpretation
Difei Gu, Yunhe Gao, Mu Zhou +1
Accurate disease interpretation from radiology remains challenging due to imaging heterogeneity. Achieving expert-level diagnostic decisions requires integration of subtle image fe…
K-Prism: A Knowledge-Guided and Prompt Integrated Universal Medical Image Segmentation Model
Bangwei Guo, Yunhe Gao, Meng Ye +4
Medical image segmentation is fundamental to clinical decision-making, yet existing models remain fragmented. They are usually trained on single knowledge sources and specific to i…
RadAlign: Advancing Radiology Report Generation with Vision-Language Concept Alignment
Difei Gu, Yunhe Gao, Yang Zhou +2
Automated chest radiographs interpretation requires both accurate disease classification and detailed radiology report generation, presenting a significant challenge in the clinica…
Aligning Human Knowledge with Visual Concepts Towards Explainable Medical Image Classification
Yunhe Gao, Difei Gu, Mu Zhou +1
Although explainability is essential in the clinical diagnosis, most deep learning models still function as black boxes without elucidating their decision-making process. In this s…