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Adversarial learning-based domain adaptation algorithm for intracranial artery stenosis detection on multi-source

Yuan Gao¹, Chenbin Ma², Lishuang Guo³

¹Department of Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, 100191, Beijing, China; Department of Ophthalmology, Xuanwu Hospital, Capital Medical University, 100053, Beijing, China.

Computers in Biology and Medicine

|January 27, 2024

Summary

This summary is machine-generated.

This study introduces an adversarial learning-based domain adaptation algorithm (ALDA) for detecting intracranial arterial stenosis (ICAS) using retinal fundus images. ALDA improves detection accuracy and generalization across diverse datasets, aiding early diagnosis of cerebrovascular diseases.

Keywords:

Adversarial learning Domain adaptation Intracranial artery stenosis Multi-source dataset Retinal fundus image

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Area of Science:

Ophthalmology
Neurology
Medical Imaging

Background:

Intracranial arterial stenosis (ICAS) is a critical condition affecting cerebral blood flow.
Retinal fundus images (RFI) offer a noninvasive method for indirectly assessing cerebrovascular health.
Existing algorithms struggle with accuracy and generalization in ICAS detection from multi-source RFI.

Purpose of the Study:

To develop and evaluate an adversarial learning-based domain adaptation algorithm (ALDA) for ICAS detection using RFI.
To enhance the accuracy and generalization capabilities of ICAS detection models across diverse datasets.
To explore ALDA's potential as an auxiliary diagnostic tool for cerebrovascular diseases and diabetic retinopathy.

Main Methods:

Implementation of an adversarial learning-based domain adaptation algorithm (ALDA).
Utilizing multi-source retinal fundus image datasets for training and validation.
Comparative analysis against other deep learning algorithms for ICAS detection.

Main Results:

ALDA demonstrated significant improvements in ICAS detection accuracy and generalization.
The algorithm effectively learned robust feature representations from diverse RFI datasets.
Validated ALDA's utility in detecting diabetic retinopathy, showcasing its versatility.

Conclusions:

ALDA provides a robust and generalizable approach for ICAS detection using RFI.
The algorithm serves as a valuable auxiliary diagnostic tool for clinicians in managing cerebrovascular diseases.
Leveraging multi-source data with adversarial learning enhances diagnostic capabilities in medical imaging.