arXiv:2606. 06664v1 Announce Type: cross Abstract: Despite high accuracy, Vision Transformer (ViT) predictions can be driven by spurious cues, raising the need to understand their inner workings before safe deployment.
By Tang Li, Yanlin Chen, Mengmeng Ma, Xi Peng
SAP-ViT is a Vision Transformer that replaces the traditional global classification token with direct aggregation of last‑layer patch embeddings, enabling class predictions to be decomposed into patch‑level contributions during the forward pass. The model incorporates sparsity regularization to produce concise, readable evidence and introduces an input‑space back‑mapping mechanism to identify which input patches influence the output. Experiments on diverse medical imaging datasets show that SAP‑ViT retains strong classification performance while delivering consistent, interpretable patch‑level explanations, with ablation studies confirming the benefits of both sparsity and back‑mapping.
By Wei Zhang, Wolfgang Nejdl, Ming Tang
arXiv:2607. 02185v1 Announce Type: cross Abstract: Deep learning has achieved remarkable performance in medical image segmentation, yet it suffers from critical limitations: mathematical intractability, substantial parameter requirements, and lack of clinical interpretability.
By Mohammad Amanour Rahman
ES‑VP introduces Energy‑Shaped Visual Prompting, a method that generates image‑specific prompts through low‑rank initialization and energy‑guided dynamic adaptation. It achieves higher performance than existing single‑prompt and diverse‑prompt approaches while using far fewer parameters. Experiments on five architectures and fifteen datasets show consistent superiority, including a 2.6% accuracy gain over DAM‑VP on CLIP with 590× fewer prompt parameters.
By Can Jin, Ying Li, Jingchen Sun, Hongwu Peng, Jiahui Zhao, Yang Zhou, Lei Li, Dimitris N. Metaxas
arXiv:2512. 21414v2 Announce Type: replace-cross Abstract: Recent tool-use frameworks powered by vision-language models (VLMs) improve image understanding by grounding model predictions with specialized tools.
By Christina Liu, Alan Q. Wang, Joy Hsu, Jiajun Wu, Ehsan Adeli
arXiv:2607. 15047v1 Announce Type: cross Abstract: Mild Cognitive Impairment is a critical early stage of cognitive decline that frequently precedes Alzheimer's disease, yet its automated detection from neuropsychological drawing tests remains fundamentally constrained by data scarcity, class imbalance, and diagnostic ambiguity near clinical boundaries.
By Javad Khoramdel, Farhad Hoseyni, Amirhossein Nikoofard