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Found 4 papers, 4 papers with code
Constrained Optimization to Train Neural Networks on Critical and Under-Represented Classes
Such class imbalance is ubiquitous in clinical applications and very crucial to handle because the classes with fewer samples most often correspond to critical cases (e. g., cancer) where misclassifications can have severe consequences.
Semi-supervised Task-driven Data Augmentation for Medical Image Segmentation
In this work, we propose a novel task-driven data augmentation method for learning with limited labeled data where the synthetic data generator, is optimized for the segmentation task.
PHiSeg: Capturing Uncertainty in Medical Image Segmentation
Segmentation of anatomical structures and pathologies is inherently ambiguous.
Semi-Supervised and Task-Driven Data Augmentation
However, there is potential to improve the approach by (i) explicitly modeling deformation fields (non-affine spatial transformation) and intensity transformations and (ii) leveraging unlabelled data during the generative process.
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