Abstract:
Skin cancer is one of the most prevalent cancers worldwide, and early detection is crucial for
successful treatment. Deep learning has shown promise in automating skin lesion classification;
however, most methods require large volumes of labeled data, which are scarce and costly to
obtain in the medical domain. To address this limitation, we propose a Transformer-based Semi-
Supervised Learning (SSL) framework for skin lesion classification. Our approach employs the
Vision Transformer (ViT) as the backbone model, leveraging its strength in capturing long-range
dependencies and global contextual information from dermoscopic images. By integrating SSL,
the framework effectively utilizes both labeled and unlabeled data, thereby reducing reliance
on extensive manual annotations. We evaluate the proposed method on the publicly available
ISIC-2018 dataset. Experimental results show that our SSL framework achieves substantial
performance improvements over a fully supervised baseline. Specifically, it achieves an accuracy
of 74.3%, balanced accuracy of 62.9%, and F1 score of 62.7representing relative improvements
of 9.3%, 11.7%, and 19.9%, respectively, compared to the baseline (65.0% accuracy, 56.3%
balanced accuracy, and 52.3% F1 score). These findings demonstrate that SSL can significantly
enhance skin lesion classification when labeled data are limited. The proposed Transformerbased
SSL framework offers a promising direction for developing cost-effective, reliable, and
scalable tools to support early skin cancer detection and improve clinical decision-making.
