Improving localization-based approaches for breast cancer screening exam classificationDownload PDF

12 Apr 2019 (modified: 05 May 2023)MIDL Abstract 2019Readers: Everyone
Keywords: breast cancer, breast cancer screening exam classification, faster-rcnn, interpretability
TL;DR: Well-tuned localization models perform well at breast cancer screening exam classification
Abstract: 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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