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Found 5 papers, 0 papers with code
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 deep learning system for differential diagnosis of skin diseases
In this paper, we developed a deep learning system (DLS) to provide a differential diagnosis of skin conditions for clinical cases (skin photographs and associated medical histories).
Similar Image Search for Histopathology: SMILY
SMILY may be a useful general-purpose tool in the pathologist's arsenal, to improve the efficiency of searching large archives of histopathology images, without the need to develop and implement specific tools for each application.
Development and Validation of a Deep Learning Algorithm for Improving Gleason Scoring of Prostate Cancer
For prostate cancer patients, the Gleason score is one of the most important prognostic factors, potentially determining treatment independent of the stage.
