3 citations · 8 across the 4 of their papers we have counts for
3 papers · 1 filter
PHiSeg: Capturing Uncertainty in Medical Image Segmentation
Christian F. Baumgartner, Kerem C. Tezcan, Krishna Chaitanya +6
Segmentation of anatomical structures and pathologies is inherently ambiguous. For instance, structure borders may not be clearly visible or different experts may have different st…
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…