Abstract
In this paper, we employ a deep Convolutional Neural Network (CNN) for the classification of regions of interest of malignant soft tissue lesions in mammography and show that it performs on par to experienced radiologists. The CNN was applied to 398 regions of 5\(\,\times \,\)5 cm, half of which contained a malignant lesion and the other half depicted suspicious regions in normal mammograms detected by a traditional CAD system. Four radiologists participated in the study. ROC analysis was used for evaluating results. The AUC of CNN was 0.87, which was higher than the mean AUC of the radiologists (0.84), though the difference was not significant.
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Acknowledgements
This research was funded by grant KUN 2012-5577 of the Dutch Cancer Society and supported by the Foundation of Population Screening Mid West.
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Kooi, T. et al. (2016). A Comparison Between a Deep Convolutional Neural Network and Radiologists for Classifying Regions of Interest in Mammography. In: Tingberg, A., Lång, K., Timberg, P. (eds) Breast Imaging. IWDM 2016. Lecture Notes in Computer Science(), vol 9699. Springer, Cham. https://doi.org/10.1007/978-3-319-41546-8_7
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