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Label Dropout: Improved Deep Learning Echocardiography Segmentation Using Multiple Datasets With Domain Shift and Partial Labelling
A significant challenge faced when training with multiple diverse datasets is the variation in label presence, i. e. the combined data are often partially-labelled.
Self-Supervised and Supervised Deep Learning for PET Image Reconstruction
The framework allows varying amounts and types of training data, from the case of having only one single dataset to reconstruct through to the case of having numerous measured datasets, which may or may not be paired with high-quality references.
Iterative reconstruction artefact removal using null-space networks
Incorporation of resolution modelling (RM) into iterative reconstruction produces Gibbs ringing artefacts which adversely affect clinically used metrics such as SUV${}_{\mbox{max}}$.
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