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07/16/2023
Boundary-weighted logit consistency improves calibration of segmentation networks
Neural network prediction probabilities and accuracy are often only weak...
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02/10/2022
A Field of Experts Prior for Adapting Neural Networks at Test Time
Performance of convolutional neural networks (CNNs) in image analysis ta...
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12/17/2021
Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentation
Supervised deep learning-based methods yield accurate results for medica...
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07/09/2020
Semi-supervised Task-driven Data Augmentation for Medical Image Segmentation
Supervised learning-based segmentation methods typically require a large...
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07/09/2020
Modelling the Distribution of 3D Brain MRI using a 2D Slice VAE
Probabilistic modelling has been an essential tool in medical image anal...
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06/18/2020
Contrastive learning of global and local features for medical image segmentation with limited annotations
A key requirement for the success of supervised deep learning is a large...
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04/09/2020
Test-Time Adaptable Neural Networks for Robust Medical Image Segmentation
Convolutional Neural Networks (CNNs) work very well for supervised learn...
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02/11/2019
Semi-Supervised and Task-Driven Data Augmentation
Supervised deep learning methods for segmentation require large amounts ...
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05/25/2018
A Lifelong Learning Approach to Brain MR Segmentation Across Scanners and Protocols
Convolutional neural networks (CNNs) have shown promising results on sev...
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04/12/2018