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Deep Learning for Medical Image Segmentation with Imprecise Annotation
Medical image segmentation (MIS) plays an instrumental role in medical image analysis, where considerable efforts have been devoted to automating the process.
Two-Stage Multi-task Self-Supervised Learning for Medical Image Segmentation
Self-supervised learning offers a solution by creating auxiliary learning tasks from the available dataset and then leveraging the knowledge acquired from solving auxiliary tasks to help better solve the target segmentation task.
A General Multiple Data Augmentation Based Framework for Training Deep Neural Networks
Combining multiple DA methods, namely multi-DA, for DNN training, provides a way to boost generalisation.
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