Cosmin I. Bercea
Cosmin I. Bercea
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Generalizing Unsupervised Anomaly Detection: Towards Unbiased Pathology Screening
Moving beyond hyperintensity thresholding. This paper analyzes the challenges and outlines opportunities for advancing the field of unsupervised anomaly detection.
Cosmin I. Bercea
,
Benedikt Wiestler
,
Daniel Rückert
,
Julia A Schnabel
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Reversing the Abnormal: Pseudo-Healthy Generative Networks for Anomaly Detection
Generative netowrks to reverse anomalies in medical imaging.
Cosmin I. Bercea
,
Benedikt Wiestler
,
Daniel Rueckert
,
Julia Schnabel
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Federated Disentangled Representation Learning for Unsupervised Brain Anomaly Detection
Implicit disentanglement of shape and appearance with federated learning for unsupervised brain pathology segmentation.
Cosmin I. Bercea
,
Benedikt Wiestler
,
Daniel Rückert
,
Shadi Albarqouni
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What do we learn? Debunking the Myth of Unsupervised Outlier Detection
Novel deformable auto-encoders for unsupervised outlier detection
Cosmin I. Bercea
,
Daniel Rueckert
,
Julia Schnabel
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FedDis: Disentangled Federated Learning for Unsupervised Brain Pathology Segmentation
Implicit disentanglement of shape and appearance with federated learning for unsupervised brain pathology segmentation.
Cosmin I. Bercea
,
Benedikt Wiestler
,
Daniel Rückert
,
Shadi Albarqouni
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Code
Project
Video
SHAMANN: Shared Memory Augmented Neural Networks
Multiple virtual actors cooperating through shared memory solve medical image segmentation.
Cosmin I. Bercea
,
Olivier Pauly
,
Andreas K. Maier
,
Florin C. Ghesu
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Confidence-aware Levenberg-Marquardt optimization for joint motion estimation and super-resolution
Multi-scale robust super-resolution by jointly optimizing the motion estimation and image reconstruction.
Cosmin I. Bercea
,
Andreas K. Maier
,
Thomas Koehler
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