Heatmap analysis for artificial intelligence explainability in diabetic retinopathy detection: illuminating the rationale of deep learning decisions.

ABSTRACT:

Background

The opaqueness of artificial intelligence (AI) algorithms decision processes limit their application in healthcare. Our objective was to explore discrepancies in heatmaps originated from slightly different retinal images from the same eyes of individuals with diabetes, to gain insights into the deep learning (DL) decision process.

Methods

Pairs of retinal images from the same eyes of individuals with diabetes, composed of images obtained before and after pupil dilation, underwent automatic analysis by a convolutional neural network for the presence of diabetic retinopathy (DR), output being a score ranging from 0 to 1. Gradient-based Class Activation Maps (GradCam) allowed visualization of activated areas. Pairs of images with discordant DL scores or outputs withi

SUBMITTER: Malerbi FK 

PROVIDER: S-EPMC11534741 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature