Fang Leyuan, Guo Jiahuan, He Xingxin, Li Muxing
The College of Electrical and Information Engineering, Hunan University, Changsha, Hunan, China.
The College of Engineering and Computer Science, The Australian National University, Canberra, ACT 2601, Australia.
Med Biol Eng Comput. 2022 Oct;60(10):2851-2863. doi: 10.1007/s11517-022-02627-8. Epub 2022 Aug 5.
Deep learning's great success in image classification is heavily reliant on large-scale annotated datasets. However, obtaining labels for optical coherence tomography (OCT) data requires the significant effort of professional ophthalmologists, which hinders the application of deep learning in OCT image classification. In this paper, we propose a self-supervised patient-specific features learning (SSPSF) method to reduce the amount of data required for well OCT image classification results. Specifically, the SSPSF consists of a self-supervised learning phase and a downstream OCT image classification learning phase. The self-supervised learning phase contains two self-supervised patient-specific features learning tasks. One is to learn to discriminate an OCT scan which belongs to a specific patient. The other task is to learn the invariant features related to patients. In addition, our proposed self-supervised learning model can learn inherent representations from the OCT images without any manual labels, which provides well initialization parameters for the downstream OCT image classification model. The proposed SSPSF achieves classification accuracy of 97.74% and 98.94% on the public RETOUCH dataset and AI Challenger dataset, respectively. The experimental results on two public OCT datasets show the effectiveness of the proposed method compared with other well-known OCT image classification methods with less annotated data.
深度学习在图像分类方面的巨大成功严重依赖于大规模的标注数据集。然而，为光学相干断层扫描（OCT）数据获取标签需要专业眼科医生付出巨大努力，这阻碍了深度学习在OCT图像分类中的应用。在本文中，我们提出了一种自监督的患者特定特征学习（SSPSF）方法，以减少获得良好的OCT图像分类结果所需的数据量。具体而言，SSPSF由一个自监督学习阶段和一个下游OCT图像分类学习阶段组成。自监督学习阶段包含两个自监督的患者特定特征学习任务。一个是学习区分属于特定患者的OCT扫描。另一个任务是学习与患者相关的不变特征。此外，我们提出的自监督学习模型可以从OCT图像中学习内在表示，而无需任何手动标签，这为下游OCT图像分类模型提供了良好的初始化参数。所提出的SSPSF在公共RETOUCH数据集和AI Challenger数据集上分别达到了97.74%和98.94%的分类准确率。在两个公共OCT数据集上的实验结果表明，与其他具有较少标注数据的知名OCT图像分类方法相比，该方法是有效的。
