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Deep Generative Models in the Real-World: An Open Challenge from Medical
  Imaging

Deep Generative Models in the Real-World: An Open Challenge from Medical Imaging

14 June 2018
Xiaoran Chen
Nick Pawlowski
Martin Rajchl
Ben Glocker
E. Konukoglu
    OOD
    MedIm
ArXivPDFHTML

Papers citing "Deep Generative Models in the Real-World: An Open Challenge from Medical Imaging"

6 / 6 papers shown
Title
Modality Cycles with Masked Conditional Diffusion for Unsupervised
  Anomaly Segmentation in MRI
Modality Cycles with Masked Conditional Diffusion for Unsupervised Anomaly Segmentation in MRI
Ziyun Liang
Harry Anthony
Felix Wagner
Konstantinos Kamnitsas
DiffM
MedIm
19
9
0
30 Aug 2023
Deep Generative Models for Physiological Signals: A Systematic Literature Review
Deep Generative Models for Physiological Signals: A Systematic Literature Review
Nour Neifar
Afef Mdhaffar
A. Ben-Hamadou
M. Jmaiel
MedIm
27
4
0
12 Jul 2023
Confidence-Aware and Self-Supervised Image Anomaly Localisation
Confidence-Aware and Self-Supervised Image Anomaly Localisation
Johanna P. Müller
Matthew Baugh
Jeremy Tan
Mischa Dombrowski
Bernhard Kainz
UQCV
OOD
16
4
0
23 Mar 2023
Exploring the Latent Space of Autoencoders with Interventional Assays
Exploring the Latent Space of Autoencoders with Interventional Assays
Felix Leeb
Stefan Bauer
M. Besserve
Bernhard Schölkopf
DRL
41
17
0
30 Jun 2021
Unsupervised Anomaly Localization using Variational Auto-Encoders
Unsupervised Anomaly Localization using Variational Auto-Encoders
David Zimmerer
Fabian Isensee
Jens Petersen
Simon A. A. Kohl
Klaus Maier-Hein
DRL
25
132
0
04 Jul 2019
Context-encoding Variational Autoencoder for Unsupervised Anomaly
  Detection
Context-encoding Variational Autoencoder for Unsupervised Anomaly Detection
David Zimmerer
Simon A. A. Kohl
Jens Petersen
Fabian Isensee
Klaus H. Maier-Hein
DRL
19
128
0
14 Dec 2018
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