3.6k citations · 5.7k across the 34 of their papers we have counts for
5 papers · 1 filter
Transductive image segmentation: Self-training and effect of uncertainty estimation
Konstantinos Kamnitsas, Stefan Winzeck, Evgenios N. Kornaropoulos +9
Semi-supervised learning (SSL) uses unlabeled data during training to learn better models. Previous studies on SSL for medical image segmentation focused mostly on improving model…
Learning from Partially Overlapping Labels: Image Segmentation under Annotation Shift
Gregory Filbrandt, Konstantinos Kamnitsas, David Bernstein +2
Scarcity of high quality annotated images remains a limiting factor for training accurate image segmentation models. While more and more annotated datasets become publicly availabl…
Confidence-based Out-of-Distribution Detection: A Comparative Study and Analysis
Christoph Berger, Magdalini Paschali, Ben Glocker +1
Image classification models deployed in the real world may receive inputs outside the intended data distribution. For critical applications such as clinical decision making, it is…
Distributional Gaussian Process Layers for Outlier Detection in Image Segmentation
Sebastian G. Popescu, David J. Sharp, James H. Cole +2
We propose a parameter efficient Bayesian layer for hierarchical convolutional Gaussian Processes that incorporates Gaussian Processes operating in Wasserstein-2 space to reliably…
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…