Exploring Advances in Transformers and CNN for Skin Lesion Diagnosis on Small Datasets
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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 ± 0.006), CoaT (0.780 ± 0.024) and ViT (0.771 ± 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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