Convolutional Network With Twofold Feature Augmentation for Diabetic Retinopathy Recognition From Multi-Modal Images.

Objective: With the scenario of limited labeled dataset, this paper introduces a deep learning-based approach that leverages Diabetic Retinopathy (DR) severity recognition performance using fundus images combined with wide-field swept-source optical coherence tomography angiography (SS-OCTA).

Methods: The proposed architecture comprises a backbone convolutional network associated with a Twofold Feature Augmentation mechanism, namely TFA-Net. The former includes multiple convolution blocks extracting representational features at various scales.

Bimodal learning via trilogy of skip-connection deep networks for diabetic retinopathy risk progression identification.

Background: Diabetic Retinopathy (DR) is considered a pathology of retinal vascular complications, which stays in the top causes of vision impairment and blindness. Therefore, precisely inspecting its progression enables the ophthalmologists to set up appropriate next-visit schedule and cost-effective treatment plans. In the literature, existing work only makes use of numerical attributes in Electronic Medical Records (EMR) for acquiring such kind of DR-oriented knowledge through conventional machine learning techniques, which require an exhaustive job of engineering most impactful risk factors.

Example-based super-resolution for enhancing spatial resolution of medical images.

This paper proposes an effective exampled-based super-resolution (SR) method to improve the spatial resolution of medical image heavily corrupted by noise. Based on the sparsity of patches, the reconstruction of a high-resolution (HR) patch from each low-resolution (LR) input patch can be performed with the help of a database, by solving a non-negative sparse optimization problem. The challenge is to effectively solve this problem in case of a large size database.