Feasibility study to improve deep learning in OCT diagnosis of rare retinal diseases with few-shot classification.
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.
['Yoo|Tae Keun|TK|http://orcid.org/0000-0003-0890-8614', 'Choi|Joon Yul|JY|', 'Kim|Hong Kyu|HK|']
D000077321:Deep Learning D005240:Feasibility Studies D006801:Humans D016571:Neural Networks, Computer D035583:Rare Diseases D012164:Retinal Diseases D041623:Tomography, Optical Coherence
Deep learning Rare diseases Few-shot learning Generative adversarial network Optical coherence tomography