Table 2.

Summary of convolutional neural network–based methods for breast density estimation.

Author	Method	Dataset/number	Task	Performance metric/s (value/s)	Code availability
Mohamed et al [70]	CNNa (AlexNet; transfer learning)	Private, University of Pittsburgh/200,00 DMb (multiview)	Breast density estimation	AUCc (0.9882)	—d
Ahn et al [71]	CNN (transfer learning)	Private, Seoul University Hospital/397 DM (multiview)	Breast density estimation	Correlation coefficient (0.96)	—
Xu et al [73]	CNN (scratch based)	Public, INbreast dataset/410 DM (multiview)	Breast density estimation	Accuracy (92.63%)	—
Wu et al [72]	CNN (transfer learning)	Private, New York University School of Medicine/201,179 cases (multiview)	Breast density estimation	Mean AUC (0.934)	[77]
Kallenberg et al [74]	Conventional sparse autoencoder, ie, CNN+stacked autoencoder	Private, Dutch Breast Cancer Screening Program and Mayo Mammography, Minnesota/493+668 images (multiview)	Breast density estimation and risk scoring	Mammographic texture (0.91) and AUC (0.61)	—
Ionescu et al [75]	CNN	Private dataset/67,520 DM (multiview)	Breast density estimation and risk scoring	Average match concordance index of (0.6)	—
Geras et al [76]	Multiview deep neural network	Private, New York University/886,000 image (multiview)	Breast density estimation and risk score	Mean AUC (0.735)	—

^aCNN: convolutional neural network.

^bDM: digital mammogram.

^cAUC: area under the curve.

^dNot available.