FEDI: Few-shot learning based on Earth Mover's Distance algorithm combined with deep residual network to identify diabetic retinopathy

TL;DR

This paper introduces a few-shot learning model combining deep residual networks and Earth Mover's Distance to diagnose diabetic retinopathy with high accuracy using limited data.

Contribution

It proposes a novel few-shot learning approach integrating Earth Mover's Distance with deep residual networks for diabetic retinopathy diagnosis.

Findings

Achieved 93.57% accuracy on 3-way 10-shot task

Built effective pre-training models for small sample classification

Demonstrated improved diagnosis accuracy with limited data

Abstract

Diabetic retinopathy(DR) is the main cause of blindness in diabetic patients. However, DR can easily delay the occurrence of blindness through the diagnosis of the fundus. In view of the reality, it is difficult to collect a large amount of diabetic retina data in clinical practice. This paper proposes a few-shot learning model of a deep residual network based on Earth Mover's Distance algorithm to assist in diagnosing DR. We build training and validation classification tasks for few-shot learning based on 39 categories of 1000 sample data, train deep residual networks, and obtain experience maximization pre-training models. Based on the weights of the pre-trained model, the Earth Mover's Distance algorithm calculates the distance between the images, obtains the similarity between the images, and changes the model's parameters to improve the accuracy of the training model. Finally, the experimental construction of the small sample classification task of the test set to optimize the model further, and finally, an accuracy of 93.5667% on the 3way10shot task of the diabetic retina test set. For the experimental code and results, please refer to: https://github.com/panliangrui/few-shot-learning-funds.