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Improving localization-based approaches for breast cancer screening exam classification

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arxiv 1908.00615 v1 pith:L2LGE56U submitted 2019-08-01 eess.IV cs.CVstat.ML

Improving localization-based approaches for breast cancer screening exam classification

Thibault F\'evry , Jason Phang , Nan Wu , S. Gene Kim , Linda Moy , Kyunghyun Cho , Krzysztof J. Geras This is my paper
classification eess.IV cs.CVstat.ML
keywords breastcancerscreeningclassificationexamlocalization-basedachievesaddition
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We trained and evaluated a localization-based deep CNN for breast cancer screening exam classification on over 200,000 exams (over 1,000,000 images). Our model achieves an AUC of 0.919 in predicting malignancy in patients undergoing breast cancer screening, reducing the error rate of the baseline (Wu et al., 2019a) by 23%. In addition, the models generates bounding boxes for benign and malignant findings, providing interpretable predictions.

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