Skin lesion classification with ensemble of squeeze-and-excitation networks and semi-supervised learning

Understand

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.