Vision Transformers for Low-Quality Histopathological Images: A Case Study on Squamous Cell Carcinoma Margin Classification.

Squamous cell carcinoma (SCC), a prevalent form of skin cancer, presents diagnostic challenges, particularly in resource-limited settings with a low-quality imaging infrastructure. The accurate classification of SCC margins is essential to guide effective surgical interventions and reduce recurrence rates. This study proposes a vision transformer (ViT)-based model to improve SCC margin classification by addressing the limitations of convolutional neural networks (CNNs) in analyzing low-quality histopathological images. This study introduced a transfer learning approach using a ViT architecture customized with additional flattening, batch normalization, and dense layers to enhance its capability for SCC margin classification. A performance evaluation was conducted using machine learning metrics averaged over five-fold cross-validation and comparisons were made with the leading CNN models. Ablation studies have explored the effects of architectural configuration on model performance. The ViT-based model achieved superior SCC margin classification with 0.928 ± 0.027 accuracy and 0.927 ± 0.028 AUC, surpassing the highest performing CNN model, InceptionV3 (accuracy: 0.86 ± 0.049; AUC: 0.837 ± 0.029), demonstrating robustness of ViT over CNN for low-quality histopathological images. Ablation studies have reinforced the importance of tailored architectural configurations for enhancing diagnostic performance. This study underscores the transformative potential of ViTs in histopathological analysis, especially in resource-limited settings. By enhancing diagnostic accuracy and reducing dependence on high-quality imaging and specialized expertise, it presents a scalable solution for global cancer diagnostics. Future research should prioritize optimizing ViTs for such environments and broadening their clinical applications.