Self-supervised Learning for Anomaly Detection in Fundus Image

Sang-Il Ahn; Jitae Shin

doi:10.1007/978-3-031-16525-2_15

ScienceGate Book Chapters

BOOK-CHAPTER

Self-supervised Learning for Anomaly Detection in Fundus Image

Sang-Il Ahn Jitae Shin

Year: 2022 Lecture notes in computer science Pages: 143-151 Publisher: Springer Science+Business Media

DOI: 10.1007/978-3-031-16525-2_15

Get Full-Text PDF Get Analytical Report

Keywords:

Artificial intelligence Computer science Anomaly detection Fundus (uterus) Leverage (statistics) Pattern recognition (psychology) Computer vision Anomaly (physics) Supervised learning Image (mathematics) Artificial neural network Medicine

Metrics

Cited By

0.53

FWCI (Field Weighted Citation Impact)

Refs

0.66

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

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

Anomaly Detection Techniques and Applications

Physical Sciences → Computer Science → Artificial Intelligence

Self-supervised Learning for Anomaly Detection in Fundus Image

Metrics

Citation History

Topics

Related Documents

Self-Supervised Metric Learning for Gaussian Anomaly Detection in Fundus Images

Self-Tuning Self-Supervised Image Anomaly Detection

Multilevel saliency-guided self-supervised learning for image anomaly detection

Self-supervised distributional and contrastive learning model for image anomaly detection

Self-supervised Federated Learning for Anomaly Detection