3 citations · 8 across the 4 of their papers we have counts for
6 papers
H&E-based Computational Biomarker Enables Universal EGFR Screening for Lung Adenocarcinoma
Gabriele Campanella, David Ho, Ida Häggström +4
Lung cancer is the leading cause of cancer death worldwide, with lung adenocarcinoma being the most prevalent form of lung cancer. EGFR positive lung adenocarcinomas have been show…
Semi-supervised Task-driven Data Augmentation for Medical Image Segmentation
Krishna Chaitanya, Neerav Karani, Christian F. Baumgartner +4
Supervised learning-based segmentation methods typically require a large number of annotated training data to generalize well at test time. In medical applications, curating such d…
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…
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…