Towards improved breast mass detection using dual-view mammogram matching

Breast cancer screening benefits from the visual analysis of multiple views of routine mammograms. As for clinical practice, computer-aided diagnosis (CAD) systems could be enhanced by integrating multi-view information. In this work, we propose a new multi-tasking framework that combines craniocaudal (CC) and mediolateral-oblique (MLO) mammograms for automatic breast mass detection. Rather than addressing mass recognition only, we exploit multi-tasking properties of deep networks to jointly learn mass matching and classification, towards better detection performance. Specifically, we propose a unified Siamese network that combines patch-level mass/non-mass classification and dual-view mass matching to take full advantage of multi-view information. This model is exploited in a full image detection pipeline based on You-Only-Look-Once (YOLO) region proposals. We carry out exhaustive experiments to highlight the contribution of dual-view matching for both patch-level classification and examination-level detection scenarios. Results demonstrate that mass matching highly improves the full-pipeline detection performance by outperforming conventional single-task schemes with 94.78% as Area Under the Curve (AUC) score and a classification accuracy of 0.8791. Interestingly, mass classification also improves the performance of mass matching, which proves the complementarity of both tasks. Our method further guides clinicians by providing accurate dual-view mass correspondences, which suggests that it could act as a relevant second opinion for mammogram interpretation and breast cancer diagnosis.

[1]  Xavier Lladó,et al.  Automatic mass detection in mammograms using deep convolutional neural networks , 2019, Journal of medical imaging.

[2]  Yann LeCun,et al.  Dimensionality Reduction by Learning an Invariant Mapping , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[3]  Leonid Karlinsky,et al.  Deep Learning for Automatic Detection of Abnormal Findings in Breast Mammography , 2017, DLMIA/ML-CDS@MICCAI.

[4]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

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

[6]  Alexander Rakhlin,et al.  Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis , 2018, bioRxiv.

[7]  Boaz Ophir,et al.  Automatic Dual-View Mass Detection in Full-Field Digital Mammograms , 2015, MICCAI.

[8]  Miguel Ángel Guevara-López,et al.  Convolutional neural networks for mammography mass lesion classification , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[9]  Jaime S. Cardoso,et al.  INbreast: toward a full-field digital mammographic database. , 2012, Academic radiology.

[10]  Xiaohui Xie,et al.  Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification , 2016, bioRxiv.

[11]  Rongguo Zhang,et al.  Cross-View Relation Networks for Mammogram Mass Detection , 2019, 2020 25th International Conference on Pattern Recognition (ICPR).

[12]  Sergey Ioffe,et al.  Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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

[14]  C. Lehman,et al.  Diagnostic Accuracy of Digital Screening Mammography With and Without Computer-Aided Detection. , 2015, JAMA internal medicine.

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

[16]  Nikos Komodakis,et al.  Learning to compare image patches via convolutional neural networks , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[17]  Li Liu,et al.  Breast mass classification via deeply integrating the contextual information from multi-view data , 2018, Pattern Recognit..

[18]  Mathieu Lamard,et al.  Cascaded multi-scale convolutional encoder-decoders for breast mass segmentation in high-resolution mammograms , 2019, 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[19]  Amira Jouirou,et al.  Multi-view information fusion in mammograms: A comprehensive overview , 2019, Inf. Fusion.

[20]  Behrouz Minaei,et al.  Assessment of a novel mass detection algorithm in mammograms. , 2013, Journal of cancer research and therapeutics.

[21]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[23]  Rahul Sukthankar,et al.  MatchNet: Unifying feature and metric learning for patch-based matching , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[24]  Ahmedin Jemal,et al.  Global Cancer in Women: Burden and Trends , 2017, Cancer Epidemiology, Biomarkers & Prevention.

[25]  Wei Liu,et al.  SSD: Single Shot MultiBox Detector , 2015, ECCV.

[26]  Gustavo Carneiro,et al.  Unregistered Multiview Mammogram Analysis with Pre-trained Deep Learning Models , 2015, MICCAI.

[27]  Henry Zhou,et al.  Mammogram Classification Using Convolutional Neural Networks , 2016 .

[28]  S. Duffy,et al.  The value of the second view in screening mammography. , 1996, The British journal of radiology.

[29]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[30]  Quoc V. Le,et al.  EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks , 2019, ICML.

[31]  Gregory R. Koch,et al.  Siamese Neural Networks for One-Shot Image Recognition , 2015 .

[32]  Mathieu Lamard,et al.  Two-stage multi-scale breast mass segmentation for full mammogram analysis without user intervention. , 2020 .

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

[35]  K. Straif,et al.  Breast-cancer screening--viewpoint of the IARC Working Group. , 2015, The New England journal of medicine.

[36]  Sebastian Ruder,et al.  An Overview of Multi-Task Learning in Deep Neural Networks , 2017, ArXiv.

[37]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[38]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[39]  István Csabai,et al.  Detecting and classifying lesions in mammograms with Deep Learning , 2017, Scientific Reports.

[40]  Ali Farhadi,et al.  YOLOv3: An Incremental Improvement , 2018, ArXiv.

[41]  Ayelet Akselrod-Ballin,et al.  Mammography Dual View Mass Correspondence , 2018, ArXiv.

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

[43]  Daniel Lévy,et al.  Breast Mass Classification from Mammograms using Deep Convolutional Neural Networks , 2016, ArXiv.

[44]  Li Shen,et al.  Deep Learning to Improve Breast Cancer Detection on Screening Mammography , 2017, Scientific Reports.

[45]  Yoni Choukroun,et al.  Mammogram Classification and Abnormality Detection from Nonlocal Labels using Deep Multiple Instance Neural Network , 2017, VCBM.

[46]  Xiaoqin Wang,et al.  Classification of Whole Mammogram and Tomosynthesis Images Using Deep Convolutional Neural Networks , 2018, IEEE Transactions on NanoBioscience.

[47]  Gwénolé Quellec,et al.  Multi-tasking Siamese Networks for Breast Mass Detection Using Dual-View Mammogram Matching , 2020, MLMI@MICCAI.

[48]  Gillian D Sanders,et al.  Benefits and Harms of Breast Cancer Screening: A Systematic Review. , 2015, JAMA.