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nnOOD: A Framework for Benchmarking Self-supervised Anomaly Localisation
  Methods

nnOOD: A Framework for Benchmarking Self-supervised Anomaly Localisation Methods

2 September 2022
Matthew Baugh
Jeremy Tan
Athanasios Vlontzos
Johanna P. Müller
Bernhard Kainz
    OOD
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Papers citing "nnOOD: A Framework for Benchmarking Self-supervised Anomaly Localisation Methods"

3 / 3 papers shown
Title
Bias Assessment and Data Drift Detection in Medical Image Analysis: A
  Survey
Bias Assessment and Data Drift Detection in Medical Image Analysis: A Survey
Andrea Prenner
Bernhard Kainz
37
0
0
26 Sep 2024
Anomaly detection through latent space restoration using
  vector-quantized variational autoencoders
Anomaly detection through latent space restoration using vector-quantized variational autoencoders
Sergio Naval Marimont
G. Tarroni
DRL
125
57
0
12 Dec 2020
MADGAN: unsupervised Medical Anomaly Detection GAN using multiple
  adjacent brain MRI slice reconstruction
MADGAN: unsupervised Medical Anomaly Detection GAN using multiple adjacent brain MRI slice reconstruction
Changhee Han
L. Rundo
K. Murao
T. Noguchi
Yuki Shimahara
Z. '. Milacski
S. Koshino
Evis Sala
Hideki Nakayama
Shinichi Satoh
MedIm
94
159
0
24 Jul 2020
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