Breast Density Classification with Deep Convolutional Neural Networks

Breast density classification is an essential part of breast cancer screening. Although a lot of prior work considered this problem as a task for learning algorithms, to our knowledge, all of them used small and not clinically realistic data both for training and evaluation of their models. In this work, we explored the limits of this task with a data set coming from over 200,000 breast cancer screening exams. We used this data to train and evaluate a strong convolutional neural network classifier. In a reader study, we found that our model can perform this task comparably to a human expert.

[1]  Ivan Laptev,et al.  Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[2]  N. Boyd,et al.  Analysis of mammographic density and breast cancer risk from digitized mammograms. , 1998, Radiographics : a review publication of the Radiological Society of North America, Inc.

[3]  Pedro Cunha Carneiro,et al.  Breast density pattern characterization by histogram features and texture descriptors , 2017 .

[4]  N Karssemeijer,et al.  Automated classification of parenchymal patterns in mammograms. , 1998, Physics in medicine and biology.

[5]  Nico Karssemeijer,et al.  Volumetric breast density estimation from full-field digital mammograms , 2006, IEEE Trans. Medical Imaging.

[6]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[7]  Ricardo de Lima Thomaz,et al.  Feature extraction using convolutional neural network for classifying breast density in mammographic images , 2017, Medical Imaging.

[8]  Jacques Wainer,et al.  Breast Density Classification with Convolutional Neural Networks , 2016, CIARP.

[9]  P. Langenberg,et al.  Breast Imaging Reporting and Data System: inter- and intraobserver variability in feature analysis and final assessment. , 2000, AJR. American journal of roentgenology.

[10]  Jitendra Virmani,et al.  A classification framework for prediction of breast density using an ensemble of neural network classifiers , 2017 .

[11]  Kyunghyun Cho,et al.  High-Resolution Breast Cancer Screening with Multi-View Deep Convolutional Neural Networks , 2017, ArXiv.

[12]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[13]  Samuel J. Magny,et al.  Breast Imaging Reporting and Data System , 2020, Definitions.

[14]  Peter Kulchyski.  and , 2015 .

[15]  Barbara Caputo,et al.  Safety in numbers: Learning categories from few examples with multi model knowledge transfer , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[16]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[17]  Yoshua Bengio,et al.  Understanding the difficulty of training deep feedforward neural networks , 2010, AISTATS.

[18]  Olivier Alonzo-Proulx,et al.  Reliability of automated breast density measurements. , 2015, Radiology.

[19]  Jennifer A. Harvey,et al.  Breast Density and Supplemental Screening , 2016 .

[20]  Andrew Zisserman,et al.  Tabula rasa: Model transfer for object category detection , 2011, 2011 International Conference on Computer Vision.

[21]  Bernt Schiele,et al.  Analysis and Optimization of Loss Functions for Multiclass, Top-k, and Multilabel Classification , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.