Bag of Tricks for Developing Diabetic Retinopathy Analysis Framework to Overcome Data Scarcity
This work addresses data scarcity in diabetic retinopathy screening for ophthalmologists, but it is incremental as it combines existing techniques rather than introducing a fundamentally new method.
The authors tackled the problem of developing a deep learning-based diabetic retinopathy analysis system using scarce UW-OCTA data by introducing robust training schemes including ensemble learning, data augmentation, and semi-supervised learning with reliable pseudo labeling, achieving 1st place in the Diabetic Retinopathy Analysis Challenge.
Recently, diabetic retinopathy (DR) screening utilizing ultra-wide optical coherence tomography angiography (UW-OCTA) has been used in clinical practices to detect signs of early DR. However, developing a deep learning-based DR analysis system using UW-OCTA images is not trivial due to the difficulty of data collection and the absence of public datasets. By realistic constraints, a model trained on small datasets may obtain sub-par performance. Therefore, to help ophthalmologists be less confused about models' incorrect decisions, the models should be robust even in data scarcity settings. To address the above practical challenging, we present a comprehensive empirical study for DR analysis tasks, including lesion segmentation, image quality assessment, and DR grading. For each task, we introduce a robust training scheme by leveraging ensemble learning, data augmentation, and semi-supervised learning. Furthermore, we propose reliable pseudo labeling that excludes uncertain pseudo-labels based on the model's confidence scores to reduce the negative effect of noisy pseudo-labels. By exploiting the proposed approaches, we achieved 1st place in the Diabetic Retinopathy Analysis Challenge.