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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Fenton, J.J., Abraham, L., Taplin, S.H., Geller, B.M., Carney, P.A., D’Orsi, C., Elmore, J.G., Barlow, W.E.: Effectiveness of computer-aided detection in community mammography practice. J. Natl. Cancer Inst. 103, 1152–1161 (2011)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 25, 1097–1105 (2012)
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015)
Silver, D., Huang, A., Maddison, C.J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al.: Mastering the game of go with deep neural networks and tree search. Nature 529(7587), 484–489 (2016)
Karssemeijer, N., te Brake, G.M.: Detection of stellate distortions in mammograms. IEEE Trans. Med. Imaging 15, 611–619 (1996)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2014). arXiv:14091556
Dauphin, Y.N., de Vries, H., Chung, J., Bengio, Y.: RMSProp and equilibrated adaptive learning rates for non-convex optimization (2015). arXiv:150204390
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: Surpassing human-level performance on imagenet classification (2015). arXiv:150201852v1
Hillis, S.L., Berbaum, K.S., Metz, C.E.: Recent developments in the Dorfman-Berbaum-Metz procedure for multireader ROC study analysis. Acad. Radiol. 15, 647–661 (2008)
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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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DOI: https://doi.org/10.1007/978-3-319-41546-8_7
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