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AI-based Anomaly Detection for Clinical-Grade Histopathological
  Diagnostics

AI-based Anomaly Detection for Clinical-Grade Histopathological Diagnostics

21 June 2024
Jonas Dippel
Niklas Prenißl
Julius Hense
Philipp Liznerski
Tobias Winterhoff
S. Schallenberg
Marius Kloft
Oliver Buchstab
David Horst
Maximilian Alber
Lukas Ruff
Klaus-Robert Müller
Frederick Klauschen
ArXivPDFHTML

Papers citing "AI-based Anomaly Detection for Clinical-Grade Histopathological Diagnostics"

5 / 5 papers shown
Title
Reimagining Anomalies: What If Anomalies Were Normal?
Reimagining Anomalies: What If Anomalies Were Normal?
Philipp Liznerski
Saurabh Varshneya
Ece Calikus
Sophie Fellenz
Marius Kloft
33
4
0
22 Feb 2024
Learning image representations for anomaly detection: application to
  discovery of histological alterations in drug development
Learning image representations for anomaly detection: application to discovery of histological alterations in drug development
I. Zingman
B. Stierstorfer
C. Lempp
Fabian Heinemann
OOD
MedIm
17
11
0
14 Oct 2022
Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero
  Outlier Images
Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier Images
Philipp Liznerski
Lukas Ruff
Robert A. Vandermeulen
Billy Joe Franks
Klaus-Robert Muller
Marius Kloft
UQCV
40
44
0
23 May 2022
Natural Synthetic Anomalies for Self-Supervised Anomaly Detection and
  Localization
Natural Synthetic Anomalies for Self-Supervised Anomaly Detection and Localization
Hannah M. Schlüter
Jeremy Tan
Benjamin Hou
Bernhard Kainz
118
128
0
30 Sep 2021
Image Synthesis as a Pretext for Unsupervised Histopathological
  Diagnosis
Image Synthesis as a Pretext for Unsupervised Histopathological Diagnosis
Dejan Štepec
D. Skočaj
MedIm
22
6
0
28 Apr 2021
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