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.
no code implementations • 17 May 2022 • Florian Kofler, Ivan Ezhov, Lucas Fidon, Izabela Horvath, Ezequiel de la Rosa, John LaMaster, Hongwei Li, Tom Finck, Suprosanna Shit, Johannes Paetzold, Spyridon Bakas, Marie Piraud, Jan Kirschke, Tom Vercauteren, Claus Zimmer, Benedikt Wiestler, Bjoern Menze
To approximate human quality ratings on scarce expert data, we train surrogate quality estimation models.
1 code implementation • 19 Mar 2022 • Suprosanna Shit, Rajat Koner, Bastian Wittmann, Johannes Paetzold, Ivan Ezhov, Hongwei Li, Jiazhen Pan, Sahand Sharifzadeh, Georgios Kaissis, Volker Tresp, Bjoern Menze
We leverage direct set-based object prediction and incorporate the interaction among the objects to learn an object-relation representation jointly.
1 code implementation • 7 Nov 2021 • Ivan Ezhov, Kevin Scibilia, Katharina Franitza, Felix Steinbauer, Suprosanna Shit, Lucas Zimmer, Jana Lipkova, Florian Kofler, Johannes Paetzold, Luca Canalini, Diana Waldmannstetter, Martin Menten, Marie Metz, Benedikt Wiestler, Bjoern Menze
Current treatment planning of patients diagnosed with a brain tumor, such as glioma, could significantly benefit by accessing the spatial distribution of tumor cell concentration.
First, it is time-consuming - the clinician needs to scan through several slices of images to ascertain the region of interest before deciding on what severity grade to assign to a patient.
no code implementations • 10 Mar 2021 • Florian Kofler, Ivan Ezhov, Fabian Isensee, Fabian Balsiger, Christoph Berger, Maximilian Koerner, Johannes Paetzold, Hongwei Li, Suprosanna Shit, Richard McKinley, Spyridon Bakas, Claus Zimmer, Donna Ankerst, Jan Kirschke, Benedikt Wiestler, Bjoern H. Menze
In this study, we explore quantitative correlates of qualitative human expert perception.
Exploiting learning algorithms under scarce data regimes is a limitation and a reality of the medical imaging field.
Multi-organ segmentation in whole-body computed tomography (CT) is a constant pre-processing step which finds its application in organ-specific image retrieval, radiotherapy planning, and interventional image analysis.