The automatic diagnosis of various retinal diseases from fundus images is important to
support clinical decision-making. However, developing such automatic solutions is …

The automatic diagnosis of various conventional ophthalmic diseases from fundus images is
important in clinical practice. However, developing such automatic solutions is challenging …

Deep learning has attracted wide attention recently because of its excellent feature
representation ability and end-to-end automatic learning method. Especially in clinical …

Most recent self-supervised methods for learning image representations focus on either
producing a global feature with invariance properties, or producing a set of local features …

Large-scale labeled data are generally required to train deep neural networks in order to
obtain better performance in visual feature learning from images or videos for computer …

Self-supervised learning provides a possible solution to extract effective visual
representations from unlabeled histopathological images. However, existing methods either …

Manually annotating medical images is extremely expensive, especially for large-scale
datasets. Self-supervised contrastive learning has been explored to learn feature …

Recently, automatic diagnostic approaches have been widely used to classify ocular
diseases. Most of these approaches are based on a single imaging modality (eg, fundus …

State-of-the-art deep learning models are often trained with a large amount of costly labeled
training data. However, requiring exhaustive manual annotations may degrade the model's …

We present an information-theoretically motivated constraint for self-supervised
representation learning from multiple related domains. In contrast to previous self …