Search Results for author:
Found 6 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.
Deep learning-based survival prediction for multiple cancer types using histopathology images
Our analysis demonstrates the potential for this approach to provide prognostic information in multiple cancer types, and even within specific pathologic stages.
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.
Whole-Slide Image Focus Quality: Automatic Assessment and Impact on AI Cancer Detection
OOF is often only detected upon careful review, potentially causing rescanning and workflow delays.
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.
