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Found 4 papers, 0 papers with code
Domain-specific optimization and diverse evaluation of self-supervised models for histopathology
Foundation models in histopathology that learn general representations across a wide range of tissue types, diagnoses, and magnifications offer the potential to reduce the data, compute, and technical expertise necessary to develop task-specific deep learning models with the required level of model performance.
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.
