Fine-Grained Self-Supervised Learning with Jigsaw Puzzles for Medical Image Classification
This addresses the challenge of detecting subtle differences in medical images for improved diagnosis, though it appears incremental as it builds on existing self-supervised learning techniques.
The paper tackles the problem of classifying fine-grained lesions in medical images by introducing a Fine-Grained Self-Supervised Learning (FG-SSL) method, which achieves favorable performance compared to state-of-the-art approaches on datasets like ISIC2018, APTOS2019, and ISIC2017.
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.