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Found 3 papers, 3 papers with code
PHiSeg: Capturing Uncertainty in Medical Image Segmentation
Segmentation of anatomical structures and pathologies is inherently ambiguous.
Adversarial Augmentation for Enhancing Classification of Mammography Images
Supervised deep learning relies on the assumption that enough training data is available, which presents a problem for its application to several fields, like medical imaging.
Injecting and removing malignant features in mammography with CycleGAN: Investigation of an automated adversarial attack using neural networks
At the higher resolution, all radiologists showed significantly lower detection rate of cancer in the modified images (0. 77-0. 84 vs. 0. 59-0. 69, p=0. 008), however, they were now able to reliably detect modified images due to better visibility of artifacts (0. 92, 0. 92 and 0. 97).
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