3 citations · 3 across the 1 of their papers we have counts for
3 papers · 1 filter
Semi-Supervised and Task-Driven Data Augmentation
Krishna Chaitanya, Neerav Karani, Christian Baumgartner +3
Supervised deep learning methods for segmentation require large amounts of labelled training data, without which they are prone to overfitting, not generalizing well to unseen imag…
Adversarial Augmentation for Enhancing Classification of Mammography Images
Lukas Jendele, Ondrej Skopek, Anton S. Becker +1
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. On t…
Injecting and removing malignant features in mammography with CycleGAN: Investigation of an automated adversarial attack using neural networks
Anton S. Becker, Lukas Jendele, Ondrej Skopek +4
To train a cycle-consistent generative adversarial network (CycleGAN) on mammographic data to inject or remove features of malignancy, and to determine whether t…