Topology-Aware Learning for Semi-supervised Cross Domain Retinal Artery/Vein Classification

Pixel-level Artery/Vein (A/V) classification of retinal blood
vessels is important in diagnosing and understanding a wide spectrum of
diseases. Deep learning-based methods have achieved promising results,
but manual image annotation is a labor-intensive and time-consuming
process and annotations between different datasets cannot be fully utilized.
The model performance might be significantly decreased when
training on one dataset but testing on another. Some unsupervised domain
adaptation (UDA) methods can alleviate this problem. However,
annotating a few target samples is usually very manageable and worthwhile
especially if it improves the adaptation performance substantially.
Consequently, we propose a novel semi-supervised retinal A/V classification
method where a few labeled target samples and the labeled publiclyaccessible
source dataset are available. We first employ the teacherstudent
framework to achieve semi-supervised learning. Then, a new
regional mixing method is proposed to reduce the domain gap at the
region level, which takes into account the topological features of retinal
vasculature. Furthermore, we combine spatial transformation with
regional mixing to generate additional synthetic images, thus improving
the generalization ability of the model.We evaluate the proposed method
on a publicly accessible (DRIVE-AV) dataset and a private dataset, and
the results show that the proposed method achieves state-of-the-art performance
for A/V classification.