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


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.

Manuscript for the International Skin Imaging Collaboration 2018
Shunsuke Kitada, Ph.D.
Shunsuke Kitada, Ph.D.
Research Scientist working on Vision & Language with Deep Learning

My research interests include deep learning-based natural language processing, computer vision, medical image processing, and computational advertising.