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Found 3 papers, 3 papers with code
Orthogonal Ensemble Networks for Biomedical Image Segmentation
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.
Post-DAE: Anatomically Plausible Segmentation via Post-Processing with Denoising Autoencoders
We introduce Post-DAE, a post-processing method based on denoising autoencoders (DAE) to improve the anatomical plausibility of arbitrary biomedical image segmentation algorithms.
Anatomical Priors for Image Segmentation via Post-Processing with Denoising Autoencoders
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.
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