1 code implementation • 22 May 2021 • Agostina J. Larrazabal, César Martínez, Jose Dolz, Enzo Ferrante
Despite the astonishing performance of deep-learning based approaches for visual tasks such as semantic segmentation, they are known to produce miscalibrated predictions, which could be harmful for critical decision-making processes.
1 code implementation • 24 Jun 2020 • Agostina J. Larrazabal, César Martínez, Ben Glocker, Enzo Ferrante
We introduce Post-DAE, a post-processing method based on denoising autoencoders (DAE) to improve the anatomical plausibility of arbitrary biomedical image segmentation algorithms.
1 code implementation • 5 Jun 2019 • Agostina J. Larrazabal, Cesar Martinez, Enzo Ferrante
We learn a low-dimensional space of anatomically plausible segmentations, and use it as a post-processing step to impose shape constraints on the resulting masks obtained with arbitrary segmentation methods.