430 citations · 721 across the 12 of their papers we have counts for
10 papers · 1 filter
Spatially Varying Label Smoothing: Capturing Uncertainty from Expert Annotations
Mobarakol Islam, Ben Glocker
The task of image segmentation is inherently noisy due to ambiguities regarding the exact location of boundaries between anatomical structures. We argue that this information can b…
Analyzing Overfitting under Class Imbalance in Neural Networks for Image Segmentation
Zeju Li, Konstantinos Kamnitsas, Ben Glocker
Class imbalance poses a challenge for developing unbiased, accurate predictive models. In particular, in image segmentation neural networks may overfit to the foreground samples fr…
Post-DAE: Anatomically Plausible Segmentation via Post-Processing with Denoising Autoencoders
Agostina J Larrazabal, César Martínez, Ben Glocker +1
We introduce Post-DAE, a post-processing method based on denoising autoencoders (DAE) to improve the anatomical plausibility of arbitrary biomedical image segmentation algorithms.…
Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty
Miguel Monteiro, Loïc Le Folgoc, Daniel Coelho de Castro +5
In image segmentation, there is often more than one plausible solution for a given input. In medical imaging, for example, experts will often disagree about the exact location of o…
Unpaired Multi-modal Segmentation via Knowledge Distillation
Qi Dou, Quande Liu, Pheng Ann Heng +1
Multi-modal learning is typically performed with network architectures containing modality-specific layers and shared layers, utilizing co-registered images of different modalities…
Domain Generalization via Model-Agnostic Learning of Semantic Features
Qi Dou, Daniel C. Castro, Konstantinos Kamnitsas +1
Generalization capability to unseen domains is crucial for machine learning models when deploying to real-world conditions. We investigate the challenging problem of domain general…