  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.
