Identifying the key components in ResNet-50 for diabetic retinopathy grading from fundus images: a systematic investigation

TL;DR

This study systematically investigates key training components of ResNet-50 for diabetic retinopathy grading, revealing their impact on performance and achieving state-of-the-art results on the EyePACS dataset using only image-level labels.

Contribution

It identifies critical training factors affecting DR grading performance and demonstrates an optimal combination that achieves state-of-the-art results without specialized network design.

Findings

Input resolution, objective function, and data augmentation significantly affect performance.

Using mean square error loss improves quadratically-weighted Kappa.

Eye pair utilization enhances DR grading accuracy.

Abstract

Although deep learning based diabetic retinopathy (DR) classification methods typically benefit from well-designed architectures of convolutional neural networks, the training setting also has a non-negligible impact on the prediction performance. The training setting includes various interdependent components, such as objective function, data sampling strategy and data augmentation approach. To identify the key components in a standard deep learning framework (ResNet-50) for DR grading, we systematically analyze the impact of several major components. Extensive experiments are conducted on a publicly-available dataset EyePACS. We demonstrate that (1) the DR grading framework is sensitive to input resolution, objective function, and composition of data augmentation, (2) using mean square error as the loss function can effectively improve the performance with respect to a task-specific evaluation metric, namely the quadratically-weighted Kappa, (3) utilizing eye pairs boosts the performance of DR grading and (4) using data resampling to address the problem of imbalanced data distribution in EyePACS hurts the performance. Based on these observations and an optimal combination of the investigated components, our framework, without any specialized network design, achieves the state-of-the-art result (0.8631 for Kappa) on the EyePACS test set (a total of 42670 fundus images) with only image-level labels. We also examine the proposed training practices on other fundus datasets and other network architectures to evaluate their generalizability. Our codes and pre-trained model are available at https://github.com/YijinHuang/pytorch-classification.