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Found 3 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.
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