Lesion-aware attention network for diabetic nephropathy diagnosis with optical coherence tomography images.

ABSTRACT:

Purpose

For early screening of diabetic nephropathy patients, we propose a deep learning algorithm to screen high-risk patients with diabetic nephropathy from retinal images of diabetic patients.

Methods

We propose the use of attentional mechanisms to improve the model's focus on lesion-prone regions of retinal OCT images. First, the data is trained using the base network and the Grad-CAM algorithm locates image regions that have a large impact on the model output and generates a rough mask localization map. The mask is used as a auxiliary region to realize the auxiliary attention module. We then inserted the region-guided attention module into the baseline model and trained the CNN model to guide the model to better focus on relevant lesion features. The proposed model impr

SUBMITTER: Liu Y 

PROVIDER: S-EPMC10641799 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature