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Found 5 papers, 3 papers with code
Deep Learning-Based Discrete Calibrated Survival Prediction
Deep neural networks for survival prediction outper-form classical approaches in discrimination, which is the ordering of patients according to their time-of-event.
Towards Explainable End-to-End Prostate Cancer Relapse Prediction from H&E Images Combining Self-Attention Multiple Instance Learning with a Recurrent Neural Network
On the use case of prostate cancer survival prediction, using 14, 479 images and only relapse times as annotations, we reach a cumulative dynamic AUC of 0. 78 on a validation set, being on par with an expert pathologist (and an AUC of 0. 77 on a separate test set).
Deep learning-based bias transfer for overcoming laboratory differences of microscopic images
The automated analysis of medical images is currently limited by technical and biological noise and bias.
Explainable Deep Learning for Augmentation of sRNA Expression Profiles
In this study, we systematically benchmark deep learning (DL) and random forest (RF)-based metadata augmentation of tissue, age, and sex using small RNA (sRNA) expression profiles.
Deep Learning and Random Forest-Based Augmentation of sRNA Expression Profiles
Automatic data-based augmentation (generation of annotations on the base of expression data) can considerably improve the annotation quality and has not been well-studied.
