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Found 2 papers, 2 papers with code
View it like a radiologist: Shifted windows for deep learning augmentation of CT images
Our method outperforms classical intensity augmentations as well as the intensity augmentation pipeline of the popular nn-UNet on multiple datasets.
A clinically motivated self-supervised approach for content-based image retrieval of CT liver images
We address these limitations by (1) proposing a self-supervised learning framework that incorporates domain-knowledge into the training procedure and (2) providing the first representation learning explainability analysis in the context of CBIR of CT liver images.
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