A Weakly Supervised Deep Learning Approach for Leakage Detection in Fluorescein Angiography Images.

ABSTRACT:

Purpose

The purpose of this study was to design an automated algorithm that can detect fluorescence leakage accurately and quickly without the use of a large amount of labeled data.

Methods

A weakly supervised learning-based method was proposed to detect fluorescein leakage without the need for manual annotation of leakage areas. To enhance the representation of the network, a residual attention module (RAM) was designed as the core component of the proposed generator. Moreover, class activation maps (CAMs) were used to define a novel anomaly mask loss to facilitate more accurate learning of leakage areas. In addition, sensitivity, specificity, accuracy, area under the curve (AUC), and dice coefficient (DC) were used to evaluate the performance of the methods.

Results

SUBMITTER: Li W 

PROVIDER: S-EPMC8934548 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature