9.8k citations · 18.5k across the 47 of their papers we have counts for
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Continuous-Time Deep Glioma Growth Models
Jens Petersen, Fabian Isensee, Gregor Köhler +9
The ability to estimate how a tumor might evolve in the future could have tremendous clinical benefits, from improved treatment decisions to better dose distribution in radiation t…
How can we learn (more) from challenges? A statistical approach to driving future algorithm development
Tobias Roß, Pierangela Bruno, Annika Reinke +12
Challenges have become the state-of-the-art approach to benchmark image analysis algorithms in a comparative manner. While the validation on identical data sets was a great step fo…
GP-ConvCNP: Better Generalization for Convolutional Conditional Neural Processes on Time Series Data
Jens Petersen, Gregor Köhler, David Zimmerer +3
Neural Processes (NPs) are a family of conditional generative models that are able to model a distribution over functions, in a way that allows them to perform predictions at test…
The Medical Segmentation Decathlon
Michela Antonelli, Annika Reinke, Spyridon Bakas +55
International challenges have become the de facto standard for comparative assessment of image analysis algorithms given a specific task. Segmentation is so far the most widely inv…
nnDetection: A Self-configuring Method for Medical Object Detection
Michael Baumgartner, Paul F. Jaeger, Fabian Isensee +1
Simultaneous localisation and categorization of objects in medical images, also referred to as medical object detection, is of high clinical relevance because diagnostic decisions…
Common Limitations of Image Processing Metrics: A Picture Story
Annika Reinke, Minu D. Tizabi, Carole H. Sudre +90
While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation. Performance metrics are…
Are we using appropriate segmentation metrics? Identifying correlates of human expert perception for CNN training beyond rolling the DICE coefficient
Florian Kofler, Ivan Ezhov, Fabian Isensee +16
Metrics optimized in complex machine learning tasks are often selected in an ad-hoc manner. It is unknown how they align with human expert perception. We explore the correlations b…