Department of Software, Ajou University, Republic of Korea.
Department of Software, Ajou University, Republic of Korea; Department of Computer Engineering, Ajou University, Republic of Korea.
Comput Biol Med. 2024 May;174:108460. doi: 10.1016/j.compbiomed.2024.108460. Epub 2024 Apr 8.
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.
由于医学图像中细微的差异，对细粒度病变进行分类具有挑战性。这是因为在训练深度神经网络时，学习具有高度细微差异的细粒度病变特征非常困难。因此，在本文中，我们引入了用于对医学图像中的细微病变进行分类的细粒度自监督学习（FG-SSL）方法。所提出的方法通过分层块逐步学习模型，使得细粒度拼图和正则化原始图像之间的互相关接近单位矩阵。我们还为逐步细粒度学习应用分层块，以从监督学习中提取每个步骤中的不同信息，从而发现细微差异。我们的方法不需要不对称模型，也不需要负样本策略，并且对批量大小不敏感。我们在使用各种医学图像识别数据集的综合实验中评估了所提出的细粒度自监督学习方法。在我们的实验中，所提出的方法在广泛使用的 ISIC2018、APTOS2019 和 ISIC2017 数据集上与现有的最先进方法相比表现出色。
