DermNet: An Incremental Knowledge Distillation Framework for Accurate and Robust Skin Layer Segmentation

Background Accurate segmentation of skin layers in histopathological whole-slide images (WSIs) is important for identifying disease-associated structural changes and supporting computer-aided dermatopathological analysis. However, existing approaches are often restricted to single magnification levels and may inadequately capture the heterogeneous morphological characteristics of individual skin layers. We propose DermNet, a multi-resolution class-incremental learning framework designed to achieve accurate skin layer segmentation while mitigating catastrophic forgetting. Methods DermNet employ

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