Breast Cancer Diagnosis in Two-View Mammography Using End-to-End Trained EfficientNet-Based Convolutional Network

• URL: http://arxiv.org/abs/2110.01606v1
• Date: Fri, 1 Oct 2021 22:09:59 GMT
• Title: Breast Cancer Diagnosis in Two-View Mammography Using End-to-End Trained EfficientNet-Based Convolutional Network
• Authors: Daniel G.P. Petrini, Carlos Shimizu, Rosimeire A. Roela, Gabriel V. Valente, Maria A.A.K. Folgueira, Hae Yong Kim
• Abstract summary: Deep convolutional neural networks are described to diagnose breast cancer in mammograms with similar or even superior performance to that of human experts. We present one of the best techniques that consists of two transfer learnings.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Some recent studies have described deep convolutional neural networks to diagnose breast cancer in mammograms with similar or even superior performance to that of human experts. Shen et al. (2019) present one of the best techniques that consists of two transfer learnings. The first uses a model trained on natural images to create a "patch classifier" that categorizes small subimages. The second uses the patch classifier to scan the whole mammogram and create the "single-view whole-image classifier". We propose to make a third transfer learning to obtain a "two-view classifier" to use the two mammographic views: bilateral craniocaudal and mediolateral oblique. We use modern EfficientNet as the basis of our model. We "end-to-end" train the entire system using CBIS-DDSM dataset. To ensure statistical robustness, we test our system twice using: (a) 5-fold cross validation; and (b) the original training/test division of the dataset. Our technique reached an AUC of 0.934 using 5-fold cross validation (sensitivity and specificity are 85.13% at the equal error rate of ROC). Using the original dataset division, our technique achieved an AUC of 0.8483, the largest AUC reported for this problem, as far as we know.

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