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.

Graphical abstract

中文翻译：

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通过少样本分类改进深度学习在罕见视网膜疾病 OCT 诊断中的可行性研究

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，罕见疾病通常被忽视。在这里，我们证明使用生成对抗网络（GAN）的少样本学习（FSL）可以提高深度学习在罕见疾病光学相干断层扫描（OCT）诊断中的适用性。本研究包括具有大量数据集的四个主要类别和具有少量数据集的五个罕见疾病类别。在训练分类器之前，我们构建了 GAN 模型，从正常的 OCT 图像生成每种罕见疾病的病理 OCT 图像。Inception-v3 架构使用增强训练数据集进行训练，并使用独立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模型在罕见视网膜疾病的OCT诊断准确性方面显着提高，并且优于传统的DL模型、Siamese网络和原型网络。通过 FSL 提高罕见视网膜疾病诊断的准确性，临床医生可以避免在 DL 辅助下忽视罕见疾病，从而减少诊断延误和患者负担。

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