Transferring deep neural networks for the differentiation of mammographic breast lesions
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摘要
Machine learning can help differentiating benign and malignant lesions seen on mammographic images. Conventional models require handcrafting features for lesion representation. Due to insufficient medical instances, the performance of convolutional neural networks(CNNs) can be further increased. This study makes use of transfer learning for mammographic breast lesion diagnosis and deep neural network(DNN) models pre-trained with large-scale natural images are employed. The diagnosis performance is evaluated with the prediction accuracy(ACC) and the area under the curve(AUC) on average. A histologically verified database is analyzed which contains 406 lesions(230 benign and 176 malignant). Involved models include transferred DNNs(GoogLeNet and AlexNet), shallow CNNs(CNN2 and CNN3) that are fully trained with medical instances and boosted by support vector machine(SVM), and two conventional methods which combine handcrafted features and SVM for lesion diagnosis. Experimental results indicate that GoogLeNet achieves the best performance(ACC=0.81, AUC=0.88), followed by AlexNet(ACC=0.79, AUC=0.83) and CNN3(ACC=0.73, AUC=0.82). Knowledge transfer can improve the mammographic breast cancer diagnosis, while its wide application still requires further verification in medical imaging domain.
Machine learning can help differentiating benign and malignant lesions seen on mammographic images. Conventional models require handcrafting features for lesion representation. Due to insufficient medical instances, the performance of convolutional neural networks(CNNs) can be further increased. This study makes use of transfer learning for mammographic breast lesion diagnosis and deep neural network(DNN) models pre-trained with large-scale natural images are employed. The diagnosis performance is evaluated with the prediction accuracy(ACC) and the area under the curve(AUC) on average. A histologically verified database is analyzed which contains 406 lesions(230 benign and 176 malignant). Involved models include transferred DNNs(GoogLeNet and AlexNet), shallow CNNs(CNN2 and CNN3) that are fully trained with medical instances and boosted by support vector machine(SVM), and two conventional methods which combine handcrafted features and SVM for lesion diagnosis. Experimental results indicate that GoogLeNet achieves the best performance(ACC=0.81, AUC=0.88), followed by AlexNet(ACC=0.79, AUC=0.83) and CNN3(ACC=0.73, AUC=0.82). Knowledge transfer can improve the mammographic breast cancer diagnosis, while its wide application still requires further verification in medical imaging domain.
Machine learning can help differentiating benign and malignant lesions seen on mammographic images. Conventional models require handcrafting features for lesion representation. Due to insufficient medical instances, the performance of convolutional neural networks(CNNs) can be further increased. This study makes use of transfer learning for mammographic breast lesion diagnosis and deep neural network(DNN) models pre-trained with large-scale natural images are employed. The diagnosis performance is evaluated with the prediction accuracy(ACC) and the area under the curve(AUC) on average. A histologically verified database is analyzed which contains 406 lesions(230 benign and 176 malignant). Involved models include transferred DNNs(GoogLeNet and AlexNet), shallow CNNs(CNN2 and CNN3) that are fully trained with medical instances and boosted by support vector machine(SVM), and two conventional methods which combine handcrafted features and SVM for lesion diagnosis. Experimental results indicate that GoogLeNet achieves the best performance(ACC=0.81, AUC=0.88), followed by AlexNet(ACC=0.79, AUC=0.83) and CNN3(ACC=0.73, AUC=0.82). Knowledge transfer can improve the mammographic breast cancer diagnosis, while its wide application still requires further verification in medical imaging domain.
引文
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16 Hu K,Yang W,Gao X.Microcalcification diagnosis in digital mammography using extreme learning machine based on hidden Markov tree model of dual-tree complex Wavelet transform.Expert Syst Appl,2017,86:135-144
17 Samala R K,Chan H P,Hadjiiski L M,et al.Multi-task transfer learning deep convolutional neural network:Application to computeraided diagnosis of breast cancer on mammograms.Phys Med Biol,2017,62:8894
18 Tajbakhsh N,Shin J Y,Gurudu S R,et al.Convolutional neural networks for medical image analysis:Full training or fine tuning?IEEE Trans Med Imag,2016,35:1299-1312
19 Shin H C,Roth H R,Gao M,et al.Deep convolutional neural networks for computer-aided detection:CNN architectures,dataset characteristics and transfer learning.IEEE Trans Med Imag,2016,35:1285-1298
20 LeCun Y,Bengio Y,Hinton G.Deep learning.Nature,2015,521:436-444
21 Arevalo J,González F A,Ramos-Pollán R,et al.Representation learning for mammography mass lesion classification with convolutional neural networks.Comput Methods Programs Biomed,2016,127:248-257
22 Yosinski J,Clune J,Bengio Y,et al.How transferable are features in deep neural networks.Adv Neural Inform Process Syst,2014:3320-3328
23 Carneiro G,Nascimento J,Bradley A P.Unregistered multiview mammogram analysis with pre-trained deep learning models.In:Navab N,Hornegger J,Wells W,et al,Eds.Medical Image Computing and Computer-Assisted Intervention-MICCAI 2015.Lecture Notes in Computer Science,Vol.9351.Cham:Springer,2015.652-660
24 Huynh B Q,Li H,Giger M L.Digital mammographic tumor classification using transfer learning from deep convolutional neural networks.J Med Imag,2016,3:034501
25 Krizhevsky A,Sutskever I,Hinton G E.ImageNet classification with deep convolutional neural networks.Adv Neural Inform Process Syst,2012:1097-1105
26 Szegedy C,Liu W,Jia Y,et al.Going deeper with convolutions.In:IEEE conference on Computer Vision and Pattern Recognition.Boston,2015.1-9
27 Russakovsky O,Deng J,Su H,et al.ImageNet large scale visual recognition challenge.Int J Comput Vis,2015,115:211-252
28 Otsu N.A threshold selection method from gray-level histograms.Automatica,1975,11:23-27
29 Soh L K,Tsatsoulis C.Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices.IEEE Trans Geosci Remote Sens,1999,37:780-795
30 Hu M K.Visual pattern recognition by moment invariants.IRE Trans Inform Theor,1962,8:179-187
31 Jia Y,Shelhamer E,Donahue J,et al.Caffe:Convolutional architecture for fast feature embedding.In:Proceedings of the 22nd ACMinternational conference on Multimedia.Orlando,2014.675-678
32 Pan S J,Yang Q.A survey on transfer learning.IEEE Trans Knowl Data Eng,2010,22:1345-1359
33 Bergstra J,Bengio Y.Random search for hyper-parameter optimization.J Mac Learn Res,2012,13:281-305
34 Zhang Z,Dai G,Liang X,et al.Can signal-to-noise ratio perform as a baseline indicator for medical image quality assessment.IEEE Access,2018,6:11534-11543
35 Casti P,Mencattini A,Salmeri M,et al.Towards localization of malignant sites of asymmetry across bilateral mammograms.Comput Methods Programs Biomed,2017,140:11-18
36 He K,Zhang X,Ren S,et al.Deep residual learning for image recognition.In:Proceedings of the IEEE conference on computer vision and pattern recognition.Las Vegas,2016.770-778
37 Xiao T,Liu L,Li K,et al.Comparison of transferred deep neural networks in ultrasonic breast masses discrimination.Biomed Res Int,2018,2018:1-9
38 Yassin N I R,Omran S,El Houby E M F,et al.Machine learning techniques for breast cancer computer aided diagnosis using different image modalities:A systematic review.Comput Methods Programs Biomed,2018,156:25-45
2 Fan L,Strasser-Weippl K,Li J J,et al.Breast cancer in China.Lancet Oncol,2014,15:e279-e289
3 Mittal S,Kaur H,Gautam N,et al.Biosensors for breast cancer diagnosis:A review of bioreceptors,biotransducers and signal amplification strategies.Biosens Bioelectron,2017,88:217-231
4 Bahl M,Barzilay R,Yedidia A B,et al.High-risk breast lesions:Amachine learning model to predict pathologic upgrade and reduce unnecessary surgical excision.Radiology,2018,286:810-818
5 Tan M,Pu J,Zheng B.Reduction of false-positive recalls using a computerized mammographic image feature analysis scheme.Phys Med Biol,2014,59:4357-4373
6 Elter M,Horsch A.CADx of mammographic masses and clustered microcalcifications:A review.Med Phys,2009,36:2052-2068
7 Tang J,Rangayyan R M,Xu J,et al.Computer-aided detection and diagnosis of breast cancer with mammography:Recent advances.IEEE Trans Inform Technol Biomed,2009,13:236-251
8 Moura D C,Guevara López M A.An evaluation of image descriptors combined with clinical data for breast cancer diagnosis.Int J Comput Ass Rad,2013,8:561-574
9 Chang C C,Lin C J.LIBSVM:A library for support vector machines.ACM Trans Intell Syst Technol,2011,2:1-27
10 Ramos-Pollán R,Guevara-López M A,Suárez-Ortega C,et al.Discovering mammography-based machine learning classifiers for breast cancer diagnosis.J Med Syst,2012,36:2259-2269
11 Khan S,Hussain M,Aboalsamh H,et al.A comparison of different Gabor feature extraction approaches for mass classification in mammography.Multimed Tools Appl,2017,76:33-57
12 Wang Y,Li J,Gao X.Latent feature mining of spatial and marginal characteristics for mammographic mass classification.Neurocomputing,2015,144:107-118
13 Xie W,Li Y,Ma Y.Breast mass classification in digital mammography based on extreme learning machine.Neurocomputing,2016,173:930-941
14 Li Y,Chen H,Wei X,et al.Mass classification in mammograms based on two-concentric masks and discriminating texton.Pattern Recognit,2016,60:648-656
15 Benndorf M,Burnside E S,Herda C,et al.External validation of a publicly available computer assisted diagnostic tool for mammographic mass lesions with two high prevalence research datasets.Med Phys,2015,42:4987-4996
16 Hu K,Yang W,Gao X.Microcalcification diagnosis in digital mammography using extreme learning machine based on hidden Markov tree model of dual-tree complex Wavelet transform.Expert Syst Appl,2017,86:135-144
17 Samala R K,Chan H P,Hadjiiski L M,et al.Multi-task transfer learning deep convolutional neural network:Application to computeraided diagnosis of breast cancer on mammograms.Phys Med Biol,2017,62:8894
18 Tajbakhsh N,Shin J Y,Gurudu S R,et al.Convolutional neural networks for medical image analysis:Full training or fine tuning?IEEE Trans Med Imag,2016,35:1299-1312
19 Shin H C,Roth H R,Gao M,et al.Deep convolutional neural networks for computer-aided detection:CNN architectures,dataset characteristics and transfer learning.IEEE Trans Med Imag,2016,35:1285-1298
20 LeCun Y,Bengio Y,Hinton G.Deep learning.Nature,2015,521:436-444
21 Arevalo J,González F A,Ramos-Pollán R,et al.Representation learning for mammography mass lesion classification with convolutional neural networks.Comput Methods Programs Biomed,2016,127:248-257
22 Yosinski J,Clune J,Bengio Y,et al.How transferable are features in deep neural networks.Adv Neural Inform Process Syst,2014:3320-3328
23 Carneiro G,Nascimento J,Bradley A P.Unregistered multiview mammogram analysis with pre-trained deep learning models.In:Navab N,Hornegger J,Wells W,et al,Eds.Medical Image Computing and Computer-Assisted Intervention-MICCAI 2015.Lecture Notes in Computer Science,Vol.9351.Cham:Springer,2015.652-660
24 Huynh B Q,Li H,Giger M L.Digital mammographic tumor classification using transfer learning from deep convolutional neural networks.J Med Imag,2016,3:034501
25 Krizhevsky A,Sutskever I,Hinton G E.ImageNet classification with deep convolutional neural networks.Adv Neural Inform Process Syst,2012:1097-1105
26 Szegedy C,Liu W,Jia Y,et al.Going deeper with convolutions.In:IEEE conference on Computer Vision and Pattern Recognition.Boston,2015.1-9
27 Russakovsky O,Deng J,Su H,et al.ImageNet large scale visual recognition challenge.Int J Comput Vis,2015,115:211-252
28 Otsu N.A threshold selection method from gray-level histograms.Automatica,1975,11:23-27
29 Soh L K,Tsatsoulis C.Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices.IEEE Trans Geosci Remote Sens,1999,37:780-795
30 Hu M K.Visual pattern recognition by moment invariants.IRE Trans Inform Theor,1962,8:179-187
31 Jia Y,Shelhamer E,Donahue J,et al.Caffe:Convolutional architecture for fast feature embedding.In:Proceedings of the 22nd ACMinternational conference on Multimedia.Orlando,2014.675-678
32 Pan S J,Yang Q.A survey on transfer learning.IEEE Trans Knowl Data Eng,2010,22:1345-1359
33 Bergstra J,Bengio Y.Random search for hyper-parameter optimization.J Mac Learn Res,2012,13:281-305
34 Zhang Z,Dai G,Liang X,et al.Can signal-to-noise ratio perform as a baseline indicator for medical image quality assessment.IEEE Access,2018,6:11534-11543
35 Casti P,Mencattini A,Salmeri M,et al.Towards localization of malignant sites of asymmetry across bilateral mammograms.Comput Methods Programs Biomed,2017,140:11-18
36 He K,Zhang X,Ren S,et al.Deep residual learning for image recognition.In:Proceedings of the IEEE conference on computer vision and pattern recognition.Las Vegas,2016.770-778
37 Xiao T,Liu L,Li K,et al.Comparison of transferred deep neural networks in ultrasonic breast masses discrimination.Biomed Res Int,2018,2018:1-9
38 Yassin N I R,Omran S,El Houby E M F,et al.Machine learning techniques for breast cancer computer aided diagnosis using different image modalities:A systematic review.Comput Methods Programs Biomed,2018,156:25-45