IViT: A Novel Interpretable Visual Transformer for Skin Disease Detection
The clinical diagnosis of skin diseases is susceptible to interference from inter-class similarity of skin lesions, and over-reliance on clinicians'experience easily leads to subjective bias. Although existing deep learning aided diagnosis methods achieve competitive accuracy, they suffer from the black-box opacity of Vision Transformer (ViT) and poor adaptability to medical few-shot scenarios. Moreover, mainstream explainable algorithms generally face the bottleneck of significant accuracy degradation when improving interpretability. This paper proposes an interpretable ViT (IViT) constrained by Quadratic Programming (QP). The introduced pre-trained transfer learning adapts to few-shot f...
| Authors | Haibiao Li, Di Lin, Xue Jiang et al. |
|---|---|
| Primary category | eess.IV |
| Published | 22 June 2026 |
| Last updated at source | 22 September 2026 |
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