With an increase in deep learning-based methods, the call for explainability of such methods
grows, especially in high-stakes decision making areas such as medical image analysis …

Machine learning techniques used in computer-aided medical image analysis usually suffer
from the domain shift problem caused by different distributions between source/reference …

Artificial Intelligence (AI) techniques of deep learning have revolutionized the disease
diagnosis with their outstanding image classification performance. In spite of the outstanding …

Retinal screening contributes to early detection of diabetic retinopathy and timely treatment.
To facilitate the screening process, we develop a deep learning system, named DeepDR …

The use of fundus images for the early screening of eye diseases is of great clinical
importance. Due to its powerful performance, deep learning is becoming more and more …

Artificial intelligence (AI) has great potential to transform healthcare by enhancing the
workflow and productivity of clinicians, enabling existing staff to serve more patients …

A plethora of classification models for the detection of glaucoma from fundus images have
been proposed in recent years. Often trained with data from a single glaucoma clinic, they …

Image quality assessment (IQA) is significant for current techniques of image-based
computer-aided diagnosis, and fundus imaging is the chief modality for screening and …

Medical images usually suffer from image degradation in clinical practice, leading to
decreased performance of deep learning-based models. To resolve this problem, most …

The domain gap caused mainly by variable medical image quality renders a major obstacle
on the path between training a segmentation model in the lab and applying the trained …