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arXiv · 2205.15442

Exploring Advances in Transformers and CNN for Skin Lesion Diagnosis on Small Datasets

Leandro M. de Lima·Renato A. Krohling
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Abstract

Skin cancer is one of the most common types of cancer in the world. Different computer-aided diagnosis systems have been proposed to tackle skin lesion diagnosis, most of them based in deep convolutional neural networks. However, recent advances in computer vision achieved state-of-art results in many tasks, notably Transformer-based networks. We explore and evaluate advances in computer vision architectures, training methods and multimodal feature fusion for skin lesion diagnosis task. Experiments show that PiT ($0.800 \pm 0.006$), CoaT ($0.780 \pm 0.024$) and ViT ($0.771 \pm 0.018$) backbone models with MetaBlock fusion achieved state-of-art results for balanced accuracy metric in PAD-UFES-20 dataset.

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Leandro M. de Lima, Renato A. Krohling. 2022-05-30. Exploring Advances in Transformers and CNN for Skin Lesion Diagnosis on Small Datasets. https://doi.org/10.1007/978-3-031-21689-3_21

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