Skin lesion classification with ensemble of squeeze-and-excitation networks and semi-supervised learning
This work addresses melanoma detection in medical imaging, but it is incremental as it builds on existing methods.
The authors tackled skin lesion classification by fine-tuning pre-trained Squeeze-and-Excitation Networks and using semi-supervised learning with data augmentation, achieving 87.2% balanced accuracy on the ISIC2018 validation dataset.
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.