4 papers
Advancing diagnostic performance and clinical usability of neural networks via adversarial training and dual batch normalization
Tianyu Han, Sven Nebelung, Federico Pedersoli +8
Unmasking the decision-making process of machine learning models is essential for implementing diagnostic support systems in clinical practice. Here, we demonstrate that adversaria…
Radiomic Feature Stability Analysis based on Probabilistic Segmentations
Christoph Haarburger, Justus Schock, Daniel Truhn +4
Identifying image features that are robust with respect to segmentation variability and domain shift is a tough challenge in radiomics. So far, this problem has mainly been tackled…
Multi Scale Curriculum CNN for Context-Aware Breast MRI Malignancy Classification
Christoph Haarburger, Michael Baumgartner, Daniel Truhn +5
Classification of malignancy for breast cancer and other cancer types is usually tackled as an object detection problem: Individual lesions are first localized and then classified…
Image-based Survival Analysis for Lung Cancer Patients using CNNs
Christoph Haarburger, Philippe Weitz, Oliver Rippel +1
Traditional survival models such as the Cox proportional hazards model are typically based on scalar or categorical clinical features. With the advent of increasingly large image d…