High-quality semi-supervised anomaly detection with generative adversarial networks-Reference-Cited by-同舟云学术

High-quality semi-supervised anomaly detection with generative adversarial networks

Published:2023-11-09 Issue: Volume: Page:
ISSN:1861-6429
Container-title:International Journal of Computer Assisted Radiology and Surgery
language:en
Short-container-title:Int J CARS

Author:

Sato Yuki^ORCID,Sato Junya^ORCID,Tomiyama Noriyuki^ORCID,Kido Shoji^ORCID

Funder

Japan Society for the Promotion of Science

Publisher

Springer Science and Business Media LLC

Subject

Health Informatics,Radiology, Nuclear Medicine and imaging,General Medicine,Surgery,Computer Graphics and Computer-Aided Design,Computer Science Applications,Computer Vision and Pattern Recognition,Biomedical Engineering

Link

https://link.springer.com/content/pdf/10.1007/s11548-023-03031-9.pdf

Reference43 articles.

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5. Sato J, Suzuki Y, Wataya T, Nishigaki D, Kita K, Yamagata K, Tomiyama N, Kido S (2022) Anatomy-aware Self-supervised Learning for Anomaly Detection in Chest Radiographs. arXiv preprint arXiv:2205.04282

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