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Enhancing Skin Lesion Classification Generalization with Active Domain Adaptation

arXiv 2412.00702 ↗First posted 1 Dec 2024cs.CVcited by 0

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Abstract (arXiv)

We propose a method to improve the generalization of skin lesion classification models by combining self-supervised learning (SSL) and active domain adaptation (ADA). The main steps of the approach include selection of an SSL pre-trained model on natural image datasets, subsequent SSL retraining on all available skin-lesion datasets, fine-tuning of the model on source domain data with labels, and application of ADA methods on target domain data. The efficacy of the proposed approach is assessed in ten skin lesion datasets with five different ADA methods, demonstrating its potential to improve generalization in settings with different amounts of domain shifts.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 52 of its 81 references that are in the map. First posted on arXiv 1 Dec 2024.

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References · 52

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