no code implementations • 19 Mar 2024 • Karol Gotkowski, Carsten Lüth, Paul F. Jäger, Sebastian Ziegler, Lars Krämer, Stefan Denner, Shuhan Xiao, Nico Disch, Klaus H. Maier-Hein, Fabian Isensee
We relate this shortcoming to two major issues: 1) the complex nature of many methods which deeply ties them to the underlying segmentation model, thus preventing a migration to more powerful state-of-the-art models as the field progresses and 2) the lack of a systematic evaluation to validate consistent performance across the broader medical domain, resulting in a lack of trust when applying these methods to new segmentation problems.
1 code implementation • 5 May 2021 • Lynton Ardizzone, Jakob Kruse, Carsten Lüth, Niels Bracher, Carsten Rother, Ullrich Köthe
We introduce a new architecture called a conditional invertible neural network (cINN), and use it to address the task of diverse image-to-image translation for natural images.
no code implementations • 25 Sep 2019 • Lynton Ardizzone, Carsten Lüth, Jakob Kruse, Carsten Rother, Ullrich Köthe
In this work, we address the task of natural image generation guided by a conditioning input.
6 code implementations • 4 Jul 2019 • Lynton Ardizzone, Carsten Lüth, Jakob Kruse, Carsten Rother, Ullrich Köthe
We demonstrate these properties for the tasks of MNIST digit generation and image colorization.