Department of Ophthalmology, Medical Research Center, Aerospace Medical Center, Republic of Korea Air Force, 635 Danjae-ro, Sangdang-gu, Cheongju, South Korea.
Epilepsy Center, Neurological Institute, Cleveland Clinic, Cleveland, OH, USA.
Med Biol Eng Comput. 2021 Feb;59(2):401-415. doi: 10.1007/s11517-021-02321-1. Epub 2021 Jan 25.
Deep learning (DL) has been successfully applied to the diagnosis of ophthalmic diseases. However, rare diseases are commonly neglected due to insufficient data. Here, we demonstrate that few-shot learning (FSL) using a generative adversarial network (GAN) can improve the applicability of DL in the optical coherence tomography (OCT) diagnosis of rare diseases. Four major classes with a large number of datasets and five rare disease classes with a few-shot dataset are included in this study. Before training the classifier, we constructed GAN models to generate pathological OCT images of each rare disease from normal OCT images. The Inception-v3 architecture was trained using an augmented training dataset, and the final model was validated using an independent test dataset. The synthetic images helped in the extraction of the characteristic features of each rare disease. The proposed DL model demonstrated a significant improvement in the accuracy of the OCT diagnosis of rare retinal diseases and outperformed the traditional DL models, Siamese network, and prototypical network. By increasing the accuracy of diagnosing rare retinal diseases through FSL, clinicians can avoid neglecting rare diseases with DL assistance, thereby reducing diagnosis delay and patient burden.
深度学习（DL）已成功应用于眼科疾病的诊断。然而，由于数据不足，罕见病通常被忽视。在这里，我们证明使用生成对抗网络（GAN）的少样本学习（FSL）可以提高 DL 在光学相干断层扫描（OCT）诊断罕见疾病中的适用性。本研究包括四个具有大量数据集的主要类别和五个具有少量数据集的罕见疾病类别。在训练分类器之前，我们构建了 GAN 模型，以便从正常 OCT 图像生成每种罕见疾病的病理性 OCT 图像。使用增强的训练数据集训练 Inception-v3 架构，并使用独立的测试数据集验证最终模型。合成图像有助于提取每种罕见疾病的特征。所提出的 DL 模型在提高罕见视网膜疾病的 OCT 诊断准确性方面表现出色，优于传统的 DL 模型、孪生网络和原型网络。通过 FSL 提高罕见视网膜疾病的诊断准确性，临床医生可以避免在 DL 辅助下忽视罕见疾病，从而减少诊断延迟和患者负担。
