Search Results for author: Felix Meissen

Found 4 papers, 4 papers with code

Unsupervised Anomaly Localization with Structural Feature-Autoencoders

1 code implementation23 Aug 2022 Felix Meissen, Johannes Paetzold, Georgios Kaissis, Daniel Rueckert

Most commonly, the anomaly detection model generates a "normal" version of an input image, and the pixel-wise $l^p$-difference of the two is used to localize anomalies.

Unsupervised Anomaly Detection

On the Pitfalls of Using the Residual Error as Anomaly Score

1 code implementation8 Feb 2022 Felix Meissen, Benedikt Wiestler, Georgios Kaissis, Daniel Rueckert

Many current state-of-the-art methods for anomaly localization in medical images rely on calculating a residual image between a potentially anomalous input image and its "healthy" reconstruction.

AutoSeg -- Steering the Inductive Biases for Automatic Pathology Segmentation

1 code implementation24 Jan 2022 Felix Meissen, Georgios Kaissis, Daniel Rueckert

In medical imaging, un-, semi-, or self-supervised pathology detection is often approached with anomaly- or out-of-distribution detection methods, whose inductive biases are not intentionally directed towards detecting pathologies, and are therefore sub-optimal for this task.

Out-of-Distribution Detection

Challenging Current Semi-Supervised Anomaly Segmentation Methods for Brain MRI

1 code implementation13 Sep 2021 Felix Meissen, Georgios Kaissis, Daniel Rueckert

In this work, we tackle the problem of Semi-Supervised Anomaly Segmentation (SAS) in Magnetic Resonance Images (MRI) of the brain, which is the task of automatically identifying pathologies in brain images.

Anomaly Detection

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