Enhancing Skin Lesion Classification Generalization with Active Domain Adaptation
This work addresses generalization issues in medical imaging for skin lesion diagnosis, but it appears incremental as it combines existing methods without introducing a fundamentally new approach.
The paper tackled the problem of improving generalization in skin lesion classification by combining self-supervised learning and active domain adaptation, resulting in demonstrated potential to enhance performance across ten datasets with varying domain shifts.
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.