Improving localization-based approaches for breast cancer screening exam classification

Apr 12, 2019 MIDL 2019 Conference Abstract Submission readers: everyone Show Bibtex
  • 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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