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.

这篇研究论文介绍了一种用于医学图像中分类微细病变的细粒度自我监督学习（FG-SSL）方法，通过逐层分块逐渐学习模型，使细粒度拼图与规范原始图像的交叉相关性接近于单位矩阵，并应用于逐步细粒度学习以发现微小差异，该方法不依赖于非对称模型、负采样策略或批量大小，并在使用多种医学图像识别数据集的全面实验中表现出优越性能。