Fine-Grained Self-Supervised Learning with Jigsaw Puzzles for Medical Image Classification

• URL: http://arxiv.org/abs/2308.05770v1
• Date: Thu, 10 Aug 2023 02:08:15 GMT
• Title: Fine-Grained Self-Supervised Learning with Jigsaw Puzzles for Medical Image Classification
• Authors: Wongi Park and Jongbin Ryu
• Abstract summary: Classifying fine-grained lesions is challenging due to minor and subtle differences in medical images. We introduce Fine-Grained Self-Supervised Learning(FG-SSL) method for classifying subtle lesions in medical images. We evaluate the proposed fine-grained self-supervised learning method on comprehensive experiments using various medical image recognition datasets.
• Score: 11.320414512937946
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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