Self-supervised anomaly detection, staging and segmentation for retinal images

Yiyue Li; Qicheng Lao; Qingbo Kang; Zekun Jiang; Shiyi Du; Shaoting Zhang; Kang Li

doi:10.1016/j.media.2023.102805

ScienceGate Book Chapters

JOURNAL ARTICLE

Self-supervised anomaly detection, staging and segmentation for retinal images

Yiyue Li Qicheng Lao Qingbo Kang Zekun Jiang Shiyi Du Shaoting Zhang Kang Li

Year: 2023 Journal: Medical Image Analysis Vol: 87 Pages: 102805-102805 Publisher: Elsevier BV

DOI: 10.1016/j.media.2023.102805

Get Full-Text PDF Get Analytical Report

Keywords:

Computer science Anomaly detection Artificial intelligence Segmentation Pattern recognition (psychology) Retinal Unsupervised learning Anomaly (physics) Supervised learning Computer vision Transformation (genetics) Artificial neural network

Metrics

Cited By

6.39

FWCI (Field Weighted Citation Impact)

Refs

0.96

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Anomaly Detection Techniques and Applications

Physical Sciences → Computer Science → Artificial Intelligence

Retinal Imaging and Analysis

Health Sciences → Medicine → Radiology, Nuclear Medicine and Imaging

COVID-19 diagnosis using AI

Health Sciences → Medicine → Radiology, Nuclear Medicine and Imaging

Self-supervised anomaly detection, staging and segmentation for retinal images

Metrics

Citation History

Topics

Related Documents

Anomaly-guided weakly supervised lesion segmentation on retinal OCT images

Self-supervised Augmented Patches Segmentation for Anomaly Detection

Self-Supervised Anomaly Detection and Staging of Emphysema from CT Scans

Self-Supervised Anomaly Detection and Staging of Emphysema from CT Scans

Weakly supervised anomaly segmentation in retinal OCT images using an adversarial learning approach