  Classifying fine-grained lesions is challenging due to minor and subtle
differences in medical images. This is because learning features of
fine-grained lesions with highly minor differences is very difficult in
training deep neural networks. Therefore, in this paper, we introduce
Fine-Grained Self-Supervised Learning(FG-SSL) method for classifying subtle
lesions in medical images. The proposed method progressively learns the model
through hierarchical block such that the cross-correlation between the
fine-grained Jigsaw puzzle and regularized original images is close to the
identity matrix. We also apply hierarchical block for progressive fine-grained
learning, which extracts different information in each step, to supervised
learning for discovering subtle differences. Our method does not require an
asymmetric model, nor does a negative sampling strategy, and is not sensitive
to batch size. We evaluate the proposed fine-grained self-supervised learning
method on comprehensive experiments using various medical image recognition
datasets. In our experiments, the proposed method performs favorably compared
to existing state-of-the-art approaches on the widely-used ISIC2018, APTOS2019,
and ISIC2017 datasets.
