Identifying and Categorizing Anomalies in Retinal Imaging Data

Identifying and Categorizing Anomalies in Retinal Imaging Data

2 December 2016

Philipp Seeböck

Bianca S. Gerendas

U. Schmidt-Erfurth

Papers citing "Identifying and Categorizing Anomalies in Retinal Imaging Data"

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Title
What can Discriminator do? Towards Box-free Ownership Verification of Generative Adversarial Network Zi-Shun Huang Boheng Li Yan Cai Run Wang Shangwei Guo Liming Fang Jing Chen Lina Wang 53 11 0 29 Jul 2023
Prototypical Residual Networks for Anomaly Detection and Localization H. Zhang Zuxuan Wu Junyao Xing Zhineng Chen Yuwei Jiang UQCV AI4TS 40 62 0 05 Dec 2022
Progressive GANomaly: Anomaly detection with progressively growing GANs Djennifer K. Madzia-Madzou Hugo J. Kuijf MedIm 29 2 0 08 Jun 2022
CutPaste: Self-Supervised Learning for Anomaly Detection and Localization Chun-Liang Li Kihyuk Sohn Jinsung Yoon Tomas Pfister SSL UQCV 38 757 0 08 Apr 2021
Student-Teacher Feature Pyramid Matching for Anomaly Detection Guodong Wang Shumin Han Errui Ding Di Huang 23 218 0 07 Mar 2021
Rethinking Assumptions in Deep Anomaly Detection Lukas Ruff Robert A. Vandermeulen Billy Joe Franks Klaus-Robert Muller Marius Kloft 40 88 0 30 May 2020
Fast Distance-based Anomaly Detection in Images Using an Inception-like Autoencoder Natasa Sarafijanovic-Djukic Jesse Davis 30 24 0 12 Mar 2020
DROCC: Deep Robust One-Class Classification Sachin Goyal Aditi Raghunathan Moksh Jain H. Simhadri Prateek Jain VLM 28 161 0 28 Feb 2020
Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Images Christoph Baur Benedikt Wiestler Shadi Albarqouni Nassir Navab UQCV MedIm 13 442 0 12 Apr 2018
Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery T. Schlegl Philipp Seeböck S. Waldstein U. Schmidt-Erfurth Georg Langs MedIm GAN 45 2,213 0 17 Mar 2017