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Joining Forces for Pathology Diagnostics with AI Assistance: The EMPAIA Initiative
Over the past decade, artificial intelligence (AI) methods in pathology have advanced substantially.
Recommendations on test datasets for evaluating AI solutions in pathology
The recommendations are intended to help AI developers demonstrate the utility of their products and to help regulatory agencies and end users verify reported performance measures.
Predicting Prostate Cancer-Specific Mortality with A.I.-based Gleason Grading
's C-indices were 0. 87 and 0. 85 for continuous and discrete grading, respectively, compared to 0. 79 (95%CI 0. 71-0. 86) for GG obtained from the reports.
Interpretable Survival Prediction for Colorectal Cancer using Deep Learning
Our approach can be used to explain predictions from a prognostic deep learning model and uncover potentially-novel prognostic features that can be reliably identified by people for future validation studies.
A glass-box interactive machine learning approach for solving NP-hard problems with the human-in-the-loop
The goal of Machine Learning to automatically learn from data, extract knowledge and to make decisions without any human intervention.
