  Diabetic Retinopathy (DR) is a leading cause of vision loss in the world, and
early DR detection is necessary to prevent vision loss and support an
appropriate treatment. In this work, we leverage interactive machine learning
and introduce a joint learning framework, termed DRG-Net, to effectively learn
both disease grading and multi-lesion segmentation. Our DRG-Net consists of two
modules: (i) DRG-AI-System to classify DR Grading, localize lesion areas, and
provide visual explanations; (ii) DRG-Expert-Interaction to receive feedback
from user-expert and improve the DRG-AI-System. To deal with sparse data, we
utilize transfer learning mechanisms to extract invariant feature
representations by using Wasserstein distance and adversarial learning-based
entropy minimization. Besides, we propose a novel attention strategy at both
low- and high-level features to automatically select the most significant
lesion information and provide explainable properties. In terms of human
interaction, we further develop DRG-Net as a tool that enables expert users to
correct the system's predictions, which may then be used to update the system
as a whole. Moreover, thanks to the attention mechanism and loss functions
constraint between lesion features and classification features, our approach
can be robust given a certain level of noise in the feedback of users. We have
benchmarked DRG-Net on the two largest DR datasets, i.e., IDRID and FGADR, and
compared it to various state-of-the-art deep learning networks. In addition to
outperforming other SOTA approaches, DRG-Net is effectively updated using user
feedback, even in a weakly-supervised manner.
