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Found 3 papers, 1 papers with code
Global explainability of a deep abstaining classifier
We present a global explainability method to characterize sources of errors in the histology prediction task of our real-world multitask convolutional neural network (MTCNN)-based deep abstaining classifier (DAC), for automated annotation of cancer pathology reports from NCI-SEER registries.
Benchmarking community drug response prediction models: datasets, models, tools, and metrics for cross-dataset generalization analysis
To assess model generalization, we introduce a set of evaluation metrics that quantify both absolute performance (e. g., predictive accuracy across datasets) and relative performance (e. g., performance drop compared to within-dataset results), enabling a more comprehensive assessment of model transferability.
Why I'm not Answering: Understanding Determinants of Classification of an Abstaining Classifier for Cancer Pathology Reports
We demonstrate an abstaining classifier in a multitask setting for classifying cancer pathology reports from the NCI SEER cancer registries on six tasks of interest.
