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Semi-supervised Task-driven Data Augmentation for Medical Image Segmentation
Supervised learning-based segmentation methods typically require a large...
07/09/2020 ∙ by Krishna Chaitanya, et al. ∙
53
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Unsupervised out-of-distribution detection using kernel density estimation
Deep neural networks achieve significant advancement to the state-of-the...
06/18/2020 ∙ by Ertunc Erdil, et al. ∙
9
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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...
06/18/2020 ∙ by Krishna Chaitanya, et al. ∙
6
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Test-Time Adaptable Neural Networks for Robust Medical Image Segmentation
Convolutional Neural Networks (CNNs) work very well for supervised learn...
04/09/2020 ∙ by Neerav Karani, et al. ∙
8
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PHiSeg: Capturing Uncertainty in Medical Image Segmentation
Segmentation of anatomical structures and pathologies is inherently ambi...
06/07/2019 ∙ by Christian F. Baumgartner, et al. ∙
4
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Semi-Supervised and Task-Driven Data Augmentation
Supervised deep learning methods for segmentation require large amounts ...
02/11/2019 ∙ by Krishna Chaitanya, et al. ∙
18
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The validity of RFID badges measuring face-to-face interactions
Face-to-face interactions are important for a variety of individual beha...
11/28/2018 ∙ by Timon Elmer, et al. ∙
0
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Learning to Segment Medical Images with Scribble-Supervision Alone
Semantic segmentation of medical images is a crucial step for the quanti...
07/12/2018 ∙ by Yigit B. Can, et al. ∙
6
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A Lifelong Learning Approach to Brain MR Segmentation Across Scanners and Protocols
Convolutional neural networks (CNNs) have shown promising results on sev...
05/25/2018 ∙ by Neerav Karani, et al. ∙
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