Attention-augmented CNNs usually beat plain CNNs on two medical image datasets, with EfficientNetB5 plus hybrid attention the best, but test-set-based model selection undermines the claimed consistency.
Skin lesion classification with ensemble of squeeze-and-excitation networks and semi-supervised learning
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abstract
In this report, we introduce the outline of our system in Task 3: Disease Classification of ISIC 2018: Skin Lesion Analysis Towards Melanoma Detection. We fine-tuned multiple pre-trained neural network models based on Squeeze-and-Excitation Networks (SENet) which achieved state-of-the-art results in the field of image recognition. In addition, we used the mean teachers as a semi-supervised learning framework and introduced some specially designed data augmentation strategies for skin lesion analysis. We confirmed our data augmentation strategy improved classification performance and demonstrated 87.2% in balanced accuracy on the official ISIC2018 validation dataset.
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Systematic Integration of Attention Modules into CNNs for Accurate and Generalizable Medical Image Diagnosis
Attention-augmented CNNs usually beat plain CNNs on two medical image datasets, with EfficientNetB5 plus hybrid attention the best, but test-set-based model selection undermines the claimed consistency.