A CNN based method for automatic mass detection and classification in mammograms

被引：14

作者：

Akselrod-Balling, Ayelet ^{[1
]}

Karlinsky, Leonid ^{[1
]}

Alpert, Sharon ^{[1
]}

Hashoul, Sharbell ^{[1
]}

Ben-Ari, Rami ^{[1
]}

Barkan, Ella ^{[1
]}

机构：

[1] IBM Res, Haifa, Israel

来源：

COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING-IMAGING AND VISUALIZATION | 2019年 / 7卷 / 03期

关键词：

Image processing and analysis; medical imaging and visualisation;

D O I：

10.1080/21681163.2017.1350206

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

A novel system for detection and classification of masses in breast mammography is introduced. The system integrates a breast segmentation module together with a modified region-based convolutional network to obtain detection and classification of masses according to BI-RADS score. While most of the previous work on mass identification in breast mammography has focused on classification, this study proposes to solve both the detection and the classification problems. The method is evaluated on a large multi-centre clinical data-set and compared to ground truth annotated by expert radiologists. Preliminary experimental results show the high accuracy and efficiency obtained by the suggested network structure. As the volume and complexity of data in health care continues to accelerate generalising such an approach may have a profound impact on patient care in many applications.

引用

页码：242 / 249

页数：8

共 28 条

[1]

[Anonymous], 2015, CANC FACTS FIG 2015

[2] Representation learning for mammography mass lesion classification with convolutional neural networks [J].

Arevalo, John ;

Gonzalez, Fabio A. ;

Ramos-Pollan, Raul ;

Oliveira, Jose L. ;

Guevara Lopez, Miguel Angel .

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 2016, 127 :248-257

[3] A WEAKLY LABELED APPROACH FOR BREAST TISSUE SEGMENTATION AND BREAST DENSITY ESTIMATION IN DIGITAL MAMMOGRAPHY [J].

Ben-Ari, Rami ;

Zlotnick, Aviad ;

Hashoul, Sharbell .

2016 IEEE 13TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI), 2016, :722-725

[4] Unregistered Multiview Mammogram Analysis with Pre-trained Deep Learning Models [J].

Carneiro, Gustavo ;

Nascimento, Jacinto ;

Bradley, Andrew P. .

MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION, PT III, 2015, 9351 :652-660

[5] Histograms of oriented gradients for human detection [J].

Dalal, N ;

Triggs, B .

2005 IEEE COMPUTER SOCIETY CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, VOL 1, PROCEEDINGS, 2005, :886-893

[6]

Dhungel N, 2015, 2015 INTERNATIONAL CONFERENCE ON DIGITAL IMAGE COMPUTING: TECHNIQUES AND APPLICATIONS (DICTA), P160

[7] Scalable Object Detection using Deep Neural Networks [J].

Erhan, Dumitru ;

Szegedy, Christian ;

Toshev, Alexander ;

Anguelov, Dragomir .

2014 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2014, :2155-2162

[8] Breast Image Analysis for Risk Assessment, Detection, Diagnosis, and Treatment of Cancer [J].

Giger, Maryellen L. ;

Karssemeijer, Nico ;

Schnabel, Julia A. .

ANNUAL REVIEW OF BIOMEDICAL ENGINEERING, VOL 15, 2013, 15 :327-357

[9]

Girshick R., 2014, IEEE COMP SOC C COMP, DOI [10.1109/CVPR.2014.81, DOI 10.1109/CVPR.2014.81]

[10] Fast R-CNN [J].

Girshick, Ross .

2015 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV), 2015, :1440-1448

← 1 2 3 →