The prediction of breast cancer biopsy outcomes using two CAD approaches that both emphasize an intelligible decision process.

By M. Elter, R. Schulz-Wendtland, Thomas Wittenberg. 2007

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A case-based interpretable deep learning model for classification of mass lesions in digital mammography (2021, background): “Some studies have reported better performance in predicting malignancy from BI-RADS features [19, 6, 7].”
Learning Optimized Risk Scores (2016, background): “3% person has breast cancer Elter et al. (2007) mushroom 8,124 113 48.”
Falling Rule Lists (2014, neutral): “|{ y n } ; { x n } , H ).”
Building Interpretable Classifiers with Rules using Bayesian Analysis (2012, methodology): “We compared algorithm performance on a collection of datasets from the UCI Machine Learning Repository [13]: Tic-Tac-Toe Endgame, Mammographic Mass [11], Titanic, Breast Cancer Wisconsin (Original) [28], and Adult.”
Learning using privileged information: SV M+ and weighted SVM (2013, background): “Mammographic Mass (Elter et al., 2007): Again, the weight learning performs on par or better than the SVM on all splits for almost all subsets.”
Regularization Parameter Selections via Generalized Information Criterion (2010, methodology): “To examine the capability of two novel CAD approaches, Elter, Schulz-Wendtland, and Wittenberg (2007) recently analyzed a dataset containing 516 benign and 445 malignant instances.”
The fused lasso penalty for learning interpretable medical scoring systems (2017, background): “• Mammographic Mass Data Set is dedicated to discrimination of benign and malignant mammographic masses [16].”