Deep learning approach for breast cancer diagnosis

Breast cancer is one of the leading fatal disease worldwide with high risk control if early discovered. Conventional method for breast screening is x-ray mammography, which is known to be challenging for early detection of cancer lesions. The dense breast structure produced due to the compression process during imaging lead to difficulties to recognize small size abnormalities. Also, inter- and intra-variations of breast tissues lead to significant difficulties to achieve high diagnosis accuracy using hand-crafted features. Deep learning is an emerging machine learning technology that requires a relatively high computation power. Yet, it proved to be very effective in several difficult tasks that requires decision making at the level of human intelligence. In this paper, we develop a new network architecture inspired by the U-net structure that can be used for effective and early detection of breast cancer. Results indicate a high rate of sensitivity and specificity that indicate potential usefulness of the proposed approach in clinical use.

[1]  Hayit Greenspan,et al.  A multi-view deep learning architecture for classification of breast microcalcifications , 2016, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI).

[2]  Hiba Chougrad,et al.  Deep Convolutional Neural Networks for breast cancer screening , 2018, Comput. Methods Programs Biomed..

[3]  Ritse Mann,et al.  Automated soft tissue lesion detection and segmentation in digital mammography using a u-net deep learning network , 2018, ArXiv.

[4]  Albert Gubern-Mérida,et al.  Automated lesion detection and segmentation in digital mammography using a u-net deep learning network , 2018, Other Conferences.

[5]  Daniel L Rubin,et al.  A curated mammography data set for use in computer-aided detection and diagnosis research , 2017, Scientific Data.

[6]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[7]  Reyer Zwiggelaar,et al.  Deep learning in mammography and breast histology, an overview and future trends , 2018, Medical Image Anal..

[8]  Hui Sun,et al.  AUNet: attention-guided dense-upsampling networks for breast mass segmentation in whole mammograms , 2018, Physics in medicine and biology.

[9]  Daniel L. Rubin,et al.  Probabilistic visual search for masses within mammography images using deep learning , 2015, 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[10]  Rafik Goubran,et al.  Abnormality Detection in Mammography using Deep Convolutional Neural Networks , 2018, 2018 IEEE International Symposium on Medical Measurements and Applications (MeMeA).

[11]  Gustavo Carneiro,et al.  A deep learning approach for the analysis of masses in mammograms with minimal user intervention , 2017, Medical Image Anal..

[12]  Nico Karssemeijer,et al.  Large scale deep learning for computer aided detection of mammographic lesions , 2017, Medical Image Anal..

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

[14]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[15]  Nico Karssemeijer,et al.  Unsupervised Deep Learning Applied to Breast Density Segmentation and Mammographic Risk Scoring , 2016, IEEE Transactions on Medical Imaging.

[16]  Cheng Li,et al.  AUNet: Breast Mass Segmentation of Whole Mammograms , 2018, ArXiv.

[17]  Andrew P. Bradley,et al.  Automated Analysis of Unregistered Multi-View Mammograms With Deep Learning , 2017, IEEE Transactions on Medical Imaging.

[18]  Bram van Ginneken,et al.  A survey on deep learning in medical image analysis , 2017, Medical Image Anal..

[19]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[20]  A. Jemal,et al.  Cancer Statistics, 2008 , 2008, CA: a cancer journal for clinicians.

[21]  Miguel Ángel Guevara-López,et al.  Representation learning for mammography mass lesion classification with convolutional neural networks , 2016, Comput. Methods Programs Biomed..

[22]  Thomas Frauenfelder,et al.  Deep Learning in Mammography: Diagnostic Accuracy of a Multipurpose Image Analysis Software in the Detection of Breast Cancer , 2017, Investigative radiology.