5 citations · 9 across the 5 of their papers we have counts for
5 papers
Deep Learning-Based Discrete Calibrated Survival Prediction
Patrick Fuhlert, Anne Ernst, Esther Dietrich +3
Deep neural networks for survival prediction outper-form classical approaches in discrimination, which is the ordering of patients according to their time-of-event. Conversely, cla…
Towards Explainable End-to-End Prostate Cancer Relapse Prediction from H&E Images Combining Self-Attention Multiple Instance Learning with a Recurrent Neural Network
Esther Dietrich, Patrick Fuhlert, Anne Ernst +5
Clinical decision support for histopathology image data mainly focuses on strongly supervised annotations, which offers intuitive interpretability, but is bound by expert performan…
Deep learning-based bias transfer for overcoming laboratory differences of microscopic images
Ann-Katrin Thebille, Esther Dietrich, Martin Klaus +9
The automated analysis of medical images is currently limited by technical and biological noise and bias. The same source tissue can be represented by vastly different images if th…
Explainable Deep Learning for Augmentation of sRNA Expression Profiles
Jelena Fiosina, Maksims Fiosins, Stefan Bonn
The lack of well-structured metadata annotations complicates there-usability and interpretation of the growing amount of publicly available RNA expression data. The machine learnin…
Deep Learning and Random Forest-Based Augmentation of sRNA Expression Profiles
Jelena Fiosina, Maksims Fiosins, Stefan Bonn
The lack of well-structured annotations in a growing amount of RNA expression data complicates data interoperability and reusability. Commonly - used text mining methods extract an…