  Ophthalmic images and derivatives such as the retinal nerve fiber layer
(RNFL) thickness map are crucial for detecting and monitoring ophthalmic
diseases (e.g., glaucoma). For computer-aided diagnosis of eye diseases, the
key technique is to automatically extract meaningful features from ophthalmic
images that can reveal the biomarkers (e.g., RNFL thinning patterns) linked to
functional vision loss. However, representation learning from ophthalmic images
that links structural retinal damage with human vision loss is non-trivial
mostly due to large anatomical variations between patients. The task becomes
even more challenging in the presence of image artifacts, which are common due
to issues with image acquisition and automated segmentation. In this paper, we
propose an artifact-tolerant unsupervised learning framework termed EyeLearn
for learning representations of ophthalmic images. EyeLearn has an artifact
correction module to learn representations that can best predict artifact-free
ophthalmic images. In addition, EyeLearn adopts a clustering-guided contrastive
learning strategy to explicitly capture the intra- and inter-image affinities.
During training, images are dynamically organized in clusters to form
contrastive samples in which images in the same or different clusters are
encouraged to learn similar or dissimilar representations, respectively. To
evaluate EyeLearn, we use the learned representations for visual field
prediction and glaucoma detection using a real-world ophthalmic image dataset
of glaucoma patients. Extensive experiments and comparisons with
state-of-the-art methods verified the effectiveness of EyeLearn for learning
optimal feature representations from ophthalmic images.
