647 results match your criteria Mammography - Computer-Aided Detection


Computer-aided detection of mass in digital breast tomosynthesis using a faster region-based convolutional neural network.

Methods 2019 Feb 13. Epub 2019 Feb 13.

Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, High Education Zone, Hangzhou 310018, China. Electronic address:

Digital breast tomosynthesis (DBT) is a newly developed three-dimensional tomographic imaging modality in the field of breast cancer screening designed to alleviate the limitations of conventional digital mammography-based breast screening methods. A computer-aided detection (CAD) system was designed for masses in DBT using a faster region-based convolutional neural network (faster-RCNN). To this end, a data set was collected, including 89 patients with 105 masses. Read More

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http://dx.doi.org/10.1016/j.ymeth.2019.02.010DOI Listing
February 2019

Breast cancer detection using deep convolutional neural networks and support vector machines.

PeerJ 2019 28;7:e6201. Epub 2019 Jan 28.

Electronic & Electrical Engineering Department, University of Strathclyde, Glasgow, United Kingdom.

It is important to detect breast cancer as early as possible. In this manuscript, a new methodology for classifying breast cancer using deep learning and some segmentation techniques are introduced. A new computer aided detection (CAD) system is proposed for classifying benign and malignant mass tumors in breast mammography images. Read More

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http://dx.doi.org/10.7717/peerj.6201DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6354665PMC
January 2019

[Establishment of a deep feature-based classification model for distinguishing benign and malignant breast tumors on full-filed digital mammography].

Nan Fang Yi Ke Da Xue Xue Bao 2019 Jan;39(1):88-92

Department of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.

Objective: To develop a deep features-based model to classify benign and malignant breast lesions on full- filed digital mammography.

Methods: The data of full-filed digital mammography in both craniocaudal view and mediolateral oblique view from 106 patients with breast neoplasms were analyzed. Twenty-three handcrafted features (HCF) were extracted from the images of the breast tumors and a suitable feature set of HCF was selected using -test. Read More

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http://dx.doi.org/10.12122/j.issn.1673-4254.2019.01.14DOI Listing
January 2019
1 Read

New Frontiers: An Update on Computer-Aided Diagnosis for Breast Imaging in the Age of Artificial Intelligence.

AJR Am J Roentgenol 2019 Feb;212(2):300-307

1 Department of Radiology, New York University School of Medicine, 160 E 34th St, New York, NY 10016.

Objective: The purpose of this article is to compare traditional versus machine learning-based computer-aided detection (CAD) platforms in breast imaging with a focus on mammography, to underscore limitations of traditional CAD, and to highlight potential solutions in new CAD systems under development for the future.

Conclusion: CAD development for breast imaging is undergoing a paradigm shift based on vast improvement of computing power and rapid emergence of advanced deep learning algorithms, heralding new systems that may hold real potential to improve clinical care. Read More

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http://dx.doi.org/10.2214/AJR.18.20392DOI Listing
February 2019

A computer-aided diagnosis scheme of breast lesion classification using GLGLM and shape features: Combined-view and multi-classifiers.

Phys Med 2018 Nov 24;55:61-72. Epub 2018 Oct 24.

Department of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, China; Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Southern Medical University, Guangzhou, Guangdong 510515, China. Electronic address:

Purpose: To address high false-positive results of FFDM issue, we make the first effort to develop a computer-aided diagnosis (CAD) scheme to analyze and distinguish breast lesions.

Method: The breast lesion regions were first segmented and depicted on FFDM images from 106 patients. In this work, 11 gray-level gap-length matrix texture features and 12 shape features were extracted form craniocaudal view and mediolateral oblique view, and then Student's t-test, Fisher-score and Relief-F were introduced to select features. Read More

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http://dx.doi.org/10.1016/j.ejmp.2018.10.016DOI Listing
November 2018

Transfer Representation Learning using Inception-V3 for the Detection of Masses in Mammography.

Conf Proc IEEE Eng Med Biol Soc 2018 Jul;2018:2587-2590

Breast cancer is the most prevalent cancer among women. The most common method to detect breast cancer is mammography. However, interpreting mammography is a challenging task that requires high skills and is timeconsuming. Read More

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http://dx.doi.org/10.1109/EMBC.2018.8512750DOI Listing

Aiding the Digital Mammogram for Detecting the Breast Cancer Using Shearlet Transform and Neural Network

Asian Pac J Cancer Prev 2018 Sep 26;19(9):2665-2671. Epub 2018 Sep 26.

Department of Computer Science and Engineering, M.P.Nachimuthu M.Jaganathan Engineering College, Chennimalai, Erode-638 112, Tamilnadu, India. Email:

Objective: Breast Cancer is the most invasive disease and fatal disease next to lung cancer in human. Early detection of breast cancer is accomplished by X-ray mammography. Mammography is the most effective and efficient technique used for detection of breast cancer in women and also to improve the breast cancer prognosis. Read More

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http://dx.doi.org/10.22034/APJCP.2018.19.9.2665DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6249454PMC
September 2018
2 Reads

Detection of masses in mammograms using a one-stage object detector based on a deep convolutional neural network.

PLoS One 2018 18;13(9):e0203355. Epub 2018 Sep 18.

Department of Computer Science and Engineering, Korea University, Seoul, Republic of Korea.

Several computer aided diagnosis (CAD) systems have been developed for mammography. They are widely used in certain countries such as the U.S. Read More

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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0203355PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6143189PMC
September 2018
10 Reads

Extreme Learning Machine (ELM)-Based Classification of Benign and Malignant Cells in Breast Cancer.

Authors:
Abdullah Toprak

Med Sci Monit 2018 Sep 17;24:6537-6543. Epub 2018 Sep 17.

Department of Biomedical Engineering, Engineering Faculty, Dicle University, Diyarbakır, Turkey.

BACKGROUND Breast cancer is one of the most common cancer types in the world and is a serious threat to health. This type of cancer is complex; it is a hereditary disease and does not result from a single cause. The diagnosis of cancer starts with a biopsy. Read More

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http://dx.doi.org/10.12659/MSM.910520DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6154116PMC
September 2018
2 Reads

A review of computer aided detection in mammography.

Clin Imaging 2018 Nov - Dec;52:305-309. Epub 2018 Sep 7.

Department of Radiology, Weill Cornell Medicine, 425 E 61st Street, New York, NY 10065, United States of America.

Breast screening with mammography is widely recognized as the most effective method of detecting early breast cancer and has consistently demonstrated a 20-40% decrease in mortality among screened women. Despite this, the sensitivity of mammography ranges between 70 and 90%. Computer aided detection (CAD) is an artificial intelligence (AI) technique that utilizes pattern recognition to highlight suspicious features on imaging and marks them for the radiologist to review and interpret. Read More

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http://dx.doi.org/10.1016/j.clinimag.2018.08.014DOI Listing
January 2019
2 Reads

Breast Cancer Detection Using Infrared Thermal Imaging and a Deep Learning Model.

Sensors (Basel) 2018 Aug 25;18(9). Epub 2018 Aug 25.

Center for Basic and Applied Research, Faculty of Informatics and Management, University of Hradec Kralove, Rokitanskeho 62, Hradec Kralove 500 03, Czech Republic.

Women's breasts are susceptible to developing cancer; this is supported by a recent study from 2016 showing that 2.8 million women worldwide had already been diagnosed with breast cancer that year. The medical care of a patient with breast cancer is costly and, given the cost and value of the preservation of the health of the citizen, the prevention of breast cancer has become a priority in public health. Read More

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http://dx.doi.org/10.3390/s18092799DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6164870PMC
August 2018
10 Reads

A New Breast Border Extraction and Contrast Enhancement Technique with Digital Mammogram Images for Improved Detection of Breast Cancer

Asian Pac J Cancer Prev 2018 Aug 24;19(8):2141-2148. Epub 2018 Aug 24.

Department of Computer Science, Gauhati University, Guwahati, India. Email:

Purpose: Breast cancer can be cured if diagnosed early, with digital mammography which is one of the most effective imaging modalities for early detection. However mammogram images often come with low contrast, high background noises and artifacts, making diagnosis difficult. The purpose of this research is to preprocess mammogram images to improve results with a computer aided diagnosis system. Read More

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http://dx.doi.org/10.22034/APJCP.2018.19.8.2141DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6171404PMC
August 2018
1 Read

Early diagnosis and detection of breast cancer.

Technol Health Care 2018 ;26(4):729-759

Department of Radiology, Faculty of Medicine, University of Nis, Nis 18108, Serbia.

Background: Breast cancer is the most common malignancy in women. It is often characterized by a lack of early symptoms, which results in late detection of the disease. Detection at advanced stages of the decease implies the treatment is more difficult and uncertain. Read More

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http://dx.doi.org/10.3233/THC-181277DOI Listing
January 2019
1 Read

The past, present and future role of artificial intelligence in imaging.

Eur J Radiol 2018 Aug 22;105:246-250. Epub 2018 Jun 22.

Department of Imaging, Brighton & Sussex University Hospitals NHS Trust, Royal Sussex County Hospital, Eastern Road, Brighton, BN2 5BE, United Kingdom.

Artificial intelligence (AI) is already widely employed in various medical roles, and ongoing technological advances are encouraging more widespread use of AI in imaging. This is partly driven by the recognition of the significant frequency and clinical impact of human errors in radiology reporting, and the promise that AI can help improve the reliability as well the efficiency of imaging interpretation. AI in imaging was first envisioned in the 1960s, but initial attempts were limited by the technology of the day. Read More

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http://dx.doi.org/10.1016/j.ejrad.2018.06.020DOI Listing
August 2018
18 Reads

Framework of Computer Aided Diagnosis Systems for Cancer Classification Based on Medical Images.

J Med Syst 2018 Jul 11;42(8):157. Epub 2018 Jul 11.

Systems & Information Department, Engineering Research Division, National Research Centre, Dokki, Cairo, 12311, Egypt.

Early detection of cancer can increase patients' survivability and treatment options. Medical images such as Mammogram, Ultrasound, Magnetic Resonance Imaging, and microscopic images are the common method for cancer diagnosis. Recently, computer-aided diagnosis (CAD) systems have been used to help physicians in cancer diagnosis so that the diagnosis accuracy can be improved. Read More

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http://dx.doi.org/10.1007/s10916-018-1010-xDOI Listing
July 2018
2 Reads

Evaluation of a computer-aided detection (CAD)-enhanced 2D synthetic mammogram: comparison with standard synthetic 2D mammograms and conventional 2D digital mammography.

Clin Radiol 2018 Oct 30;73(10):886-892. Epub 2018 Jun 30.

Loughborough University, Epinal Way, Loughborough LE11 3TU, UK.

Aim: To evaluate the diagnostic performance of computer-aided detection (CAD)-enhanced synthetic mammograms in comparison with standard synthetic mammograms and full-field digital mammography (FFDM).

Materials And Methods: A CAD-enhanced synthetic mammogram, a standard synthetic mammogram, and FFDM were available in 68 breast-screening cases recalled for soft-tissue abnormalities (masses, parenchymal deformities, and asymmetric densities). Two radiologists, blinded to image type and final assessment outcome, retrospectively read oblique and craniocaudal projections for each type of mammogram. Read More

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http://dx.doi.org/10.1016/j.crad.2018.05.028DOI Listing
October 2018
2 Reads

Automated and real-time segmentation of suspicious breast masses using convolutional neural network.

PLoS One 2018 16;13(5):e0195816. Epub 2018 May 16.

Department of Radiology, Mayo Clinic College of Medicine and Science, Rochester, Minnesota, United States of America.

In this work, a computer-aided tool for detection was developed to segment breast masses from clinical ultrasound (US) scans. The underlying Multi U-net algorithm is based on convolutional neural networks. Under the Mayo Clinic Institutional Review Board protocol, a prospective study of the automatic segmentation of suspicious breast masses was performed. Read More

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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0195816PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5955504PMC
July 2018
1 Read

False-positive reduction in computer-aided mass detection using mammographic texture analysis and classification.

Comput Methods Programs Biomed 2018 Jul 31;160:75-83. Epub 2018 Mar 31.

Université de Tunis El Manar, Institut Supérieur d'Informatique, Research Team on Intelligent Systems in imaging and Artificial Vision (SIIVA), Laboratoire de recherche en Informatique, Modélisation et Traitement de l'Information et de la Connaissance (LIMTIC), 2Rue Abou Raihane Bayrouni, Ariana 2080, Tunisia. Electronic address:

Background And Objective: The aim of computer-aided-detection (CAD) systems for mammograms is to assist radiologists by marking region of interest (ROIs) depicting abnormalities. However, the confusing appearance of some normal tissues that visually look like masses results in a large proportion of marked ROIs with normal tissues. This paper copes with this problem and proposes a framework to reduce false positive masses detected by CAD. Read More

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http://dx.doi.org/10.1016/j.cmpb.2018.03.026DOI Listing
July 2018
36 Reads
1.900 Impact Factor

A computer-aided detection of the architectural distortion in digital mammograms using the fractal dimension measurements of BEMD.

Comput Med Imaging Graph 2018 Dec 3;70:173-184. Epub 2018 Apr 3.

School of Electrical, Electronic and Computer Engineering, The University of Western Australia, 35 Stirling Highway, Crawely, WA 6009, Australia. Electronic address:

Achieving a high performance for the detection and characterization of architectural distortion in screening mammograms is important for an efficient breast cancer early detection. Viewing a mammogram image as a rough surface that can be described using the fractal theory is a well-recognized approach. This paper presents a new fractal-based computer-aided detection (CAD) algorithm for characterizing various breast tissues in screening mammograms with a particular focus on distinguishing between architectural distortion and normal breast parenchyma. Read More

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http://dx.doi.org/10.1016/j.compmedimag.2018.04.001DOI Listing
December 2018
2 Reads

Applying a new computer-aided detection scheme generated imaging marker to predict short-term breast cancer risk.

Phys Med Biol 2018 May 15;63(10):105005. Epub 2018 May 15.

School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, United States of America. Author to whom any correspondence should be addressed.

This study aims to investigate the feasibility of identifying a new quantitative imaging marker based on false-positives generated by a computer-aided detection (CAD) scheme to help predict short-term breast cancer risk. An image dataset including four view mammograms acquired from 1044 women was retrospectively assembled. All mammograms were originally interpreted as negative by radiologists. Read More

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http://dx.doi.org/10.1088/1361-6560/aabefeDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5976448PMC
May 2018
6 Reads
2.760 Impact Factor

The efficacy of using computer-aided detection (CAD) for detection of breast cancer in mammography screening: a systematic review.

Acta Radiol 2019 Jan 17;60(1):13-18. Epub 2018 Apr 17.

1 Department of Diagnostic Radiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark.

Background: Early detection of breast cancer (BC) is crucial in lowering the mortality.

Purpose: To present an overview of studies concerning computer-aided detection (CAD) in screening mammography for early detection of BC and compare diagnostic accuracy and recall rates (RR) of single reading (SR) with SR + CAD and double reading (DR) with SR + CAD.

Material And Methods: PRISMA guidelines were used as a review protocol. Read More

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http://dx.doi.org/10.1177/0284185118770917DOI Listing
January 2019
7 Reads

Mass detection in digital breast tomosynthesis data using convolutional neural networks and multiple instance learning.

Comput Biol Med 2018 05 12;96:283-293. Epub 2018 Apr 12.

Department of Computer Science and Software Engineering Concordia University, 1455 De Maisonneuve Blvd. W, Montreal, Quebec H3G 1M8, Canada.

Digital breast tomosynthesis (DBT) was developed in the field of breast cancer screening as a new tomographic technique to minimize the limitations of conventional digital mammography breast screening methods. A computer-aided detection (CAD) framework for mass detection in DBT has been developed and is described in this paper. The proposed framework operates on a set of two-dimensional (2D) slices. Read More

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http://dx.doi.org/10.1016/j.compbiomed.2018.04.004DOI Listing
May 2018
5 Reads

Mass Segmentation in Automated 3-D Breast Ultrasound Using Adaptive Region Growing and Supervised Edge-Based Deformable Model.

IEEE Trans Med Imaging 2018 04;37(4):918-928

Automated 3-D breast ultrasound has been proposed as a complementary modality to mammography for early detection of breast cancers. To facilitate the interpretation of these images, computer aided detection systems are being developed in which mass segmentation is an essential component for feature extraction and temporal comparisons. However, automated segmentation of masses is challenging because of the large variety in shape, size, and texture of these 3-D objects. Read More

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http://dx.doi.org/10.1109/TMI.2017.2787685DOI Listing
April 2018
5 Reads

A hierarchical pipeline for breast boundary segmentation and calcification detection in mammograms.

Comput Biol Med 2018 05 16;96:178-188. Epub 2018 Mar 16.

School of Computing and Mathematics, Ulster University, Jordanstown, Antrim BT37 0QB, United Kingdom.

Breast cancer is one of the most common cancer risks to women in the world. Amongst multiple breast imaging modalities, mammography has been widely used in breast cancer diagnosis and screening. Quantitative analyses including breast boundary segmentation and calcification localization are essential steps in a Computer Aided Diagnosis system based on mammography analysis. Read More

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http://dx.doi.org/10.1016/j.compbiomed.2018.03.011DOI Listing
May 2018
2 Reads

Deep learning beyond cats and dogs: recent advances in diagnosing breast cancer with deep neural networks.

Br J Radiol 2018 Sep 10;91(1089):20170545. Epub 2018 Apr 10.

2 Department of Computer Science, Center for Research in Computer Vision, University of Central Florida (UCF) , Orlando, FL , USA.

Deep learning has demonstrated tremendous revolutionary changes in the computing industry and its effects in radiology and imaging sciences have begun to dramatically change screening paradigms. Specifically, these advances have influenced the development of computer-aided detection and diagnosis (CAD) systems. These technologies have long been thought of as "second-opinion" tools for radiologists and clinicians. Read More

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http://dx.doi.org/10.1259/bjr.20170545DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6223155PMC
September 2018
8 Reads

Detecting and classifying lesions in mammograms with Deep Learning.

Sci Rep 2018 Mar 15;8(1):4165. Epub 2018 Mar 15.

Department of Physics of Complex Systems, Eötvös Loránd University, Budapest, Hungary.

In the last two decades, Computer Aided Detection (CAD) systems were developed to help radiologists analyse screening mammograms, however benefits of current CAD technologies appear to be contradictory, therefore they should be improved to be ultimately considered useful. Since 2012, deep convolutional neural networks (CNN) have been a tremendous success in image recognition, reaching human performance. These methods have greatly surpassed the traditional approaches, which are similar to currently used CAD solutions. Read More

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http://dx.doi.org/10.1038/s41598-018-22437-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5854668PMC
March 2018
5 Reads

Automated and effective content-based image retrieval for digital mammography.

J Xray Sci Technol 2018 ;26(1):29-49

Department of Computer Science and Engineering, Indian Institute of Technology (BHU), Varanasi, Uttar Pradesh, India.

Nowadays, huge number of mammograms has been generated in hospitals for the diagnosis of breast cancer. Content-based image retrieval (CBIR) can contribute more reliable diagnosis by classifying the query mammograms and retrieving similar mammograms already annotated by diagnostic descriptions and treatment results. Since labels, artifacts, and pectoral muscles present in mammograms can bias the retrieval procedures, automated detection and exclusion of these image noise patterns and/or non-breast regions is an essential pre-processing step. Read More

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http://www.medra.org/servlet/aliasResolver?alias=iospress&am
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http://dx.doi.org/10.3233/XST-17306DOI Listing
September 2018
13 Reads

Simultaneous detection and classification of breast masses in digital mammograms via a deep learning YOLO-based CAD system.

Comput Methods Programs Biomed 2018 Apr 31;157:85-94. Epub 2018 Jan 31.

Department of Biomedical Engineering, College of Electronics and Information, Kyung Hee University, Yongin, Republic of Korea. Electronic address:

Background And Objective: Automatic detection and classification of the masses in mammograms are still a big challenge and play a crucial role to assist radiologists for accurate diagnosis. In this paper, we propose a novel Computer-Aided Diagnosis (CAD) system based on one of the regional deep learning techniques, a ROI-based Convolutional Neural Network (CNN) which is called You Only Look Once (YOLO). Although most previous studies only deal with classification of masses, our proposed YOLO-based CAD system can handle detection and classification simultaneously in one framework. Read More

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http://dx.doi.org/10.1016/j.cmpb.2018.01.017DOI Listing
April 2018
7 Reads

Deep Convolutional Neural Networks for breast cancer screening.

Comput Methods Programs Biomed 2018 Apr 11;157:19-30. Epub 2018 Jan 11.

Laboratory of Fundamental Mathematics (LMF), Faculty of science, University Chouaib Doukkali, El Jadida 24000, Morocco.

Background And Objective: Radiologists often have a hard time classifying mammography mass lesions which leads to unnecessary breast biopsies to remove suspicions and this ends up adding exorbitant expenses to an already burdened patient and health care system.

Methods: In this paper we developed a Computer-aided Diagnosis (CAD) system based on deep Convolutional Neural Networks (CNN) that aims to help the radiologist classify mammography mass lesions. Deep learning usually requires large datasets to train networks of a certain depth from scratch. Read More

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http://dx.doi.org/10.1016/j.cmpb.2018.01.011DOI Listing
April 2018
2 Reads

A new computer-aided detection approach based on analysis of local and global mammographic feature asymmetry.

Med Phys 2018 Apr 15;45(4):1459-1470. Epub 2018 Mar 15.

Department of Biomedical Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel.

Purpose: This study aims to develop and test a new computer-aided detection (CAD) approach and scheme, assessing the likelihood of a subject harboring breast abnormalities.

Methods: The proposed scheme is based on the analysis of both local and global bilateral mammographic feature asymmetries. The level of local or global asymmetry is assessed by analyzing mammographic features extracted from the bilaterally matched regions of interest (ROIs), or from the entire breast, respectively. Read More

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http://doi.wiley.com/10.1002/mp.12806
Publisher Site
http://dx.doi.org/10.1002/mp.12806DOI Listing
April 2018
3 Reads

Machine learning techniques for breast cancer computer aided diagnosis using different image modalities: A systematic review.

Comput Methods Programs Biomed 2018 Mar 12;156:25-45. Epub 2017 Dec 12.

Anaesthesia & Pain, Medical Division, National Research Centre, Dokki, Cairo 12311, Egypt. Electronic address:

Background And Objective: The high incidence of breast cancer in women has increased significantly in the recent years. Physician experience of diagnosing and detecting breast cancer can be assisted by using some computerized features extraction and classification algorithms. This paper presents the conduction and results of a systematic review (SR) that aims to investigate the state of the art regarding the computer aided diagnosis/detection (CAD) systems for breast cancer. Read More

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http://dx.doi.org/10.1016/j.cmpb.2017.12.012DOI Listing
March 2018
6 Reads
1.900 Impact Factor

Detection of mass regions in mammograms by bilateral analysis adapted to breast density using similarity indexes and convolutional neural networks.

Comput Methods Programs Biomed 2018 Mar 11;156:191-207. Epub 2018 Jan 11.

Pontifical Catholic University of Rio de Janeiro - PUC - Rio, R. São Vicente, 225, Gávea, Rio de Janeiro, RJ, 22453-900, Brazil. Electronic address:

Background And Objective: The processing of medical image is an important tool to assist in minimizing the degree of uncertainty of the specialist, while providing specialists with an additional source of detect and diagnosis information. Breast cancer is the most common type of cancer that affects the female population around the world. It is also the most deadly type of cancer among women. Read More

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http://dx.doi.org/10.1016/j.cmpb.2018.01.007DOI Listing
March 2018
2 Reads

Dedicated computer-aided detection software for automated 3D breast ultrasound; an efficient tool for the radiologist in supplemental screening of women with dense breasts.

Eur Radiol 2018 Jul 7;28(7):2996-3006. Epub 2018 Feb 7.

Department of Radiology and Nuclear Medicine, Radboud University Medical Centre Nijmegen (NL), Geert Grooteplein 10, 6525 GA, Nijmegen, The Netherlands.

Objectives: To determine the effect of computer-aided-detection (CAD) software for automated breast ultrasound (ABUS) on reading time (RT) and performance in screening for breast cancer.

Material And Methods: Unilateral ABUS examinations of 120 women with dense breasts were randomly selected from a multi-institutional archive of cases including 30 malignant (20/30 mammography-occult), 30 benign, and 60 normal cases with histopathological verification or ≥ 2 years of negative follow-up. Eight radiologists read once with (CAD-ABUS) and once without CAD (ABUS) with > 8 weeks between reading sessions. Read More

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http://dx.doi.org/10.1007/s00330-017-5280-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5986849PMC
July 2018
9 Reads

Classification of micro-calcification in mammograms using scalable linear Fisher discriminant analysis.

Med Biol Eng Comput 2018 Aug 25;56(8):1475-1485. Epub 2018 Jan 25.

Aberystwyth University, Aberystwyth, UK.

Breast cancer is one of the major causes of death in women. Computer Aided Diagnosis (CAD) systems are being developed to assist radiologists in early diagnosis. Micro-calcifications can be an early symptom of breast cancer. Read More

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http://dx.doi.org/10.1007/s11517-017-1774-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6061516PMC
August 2018
3 Reads

Locally adaptive decision in detection of clustered microcalcifications in mammograms.

Phys Med Biol 2018 02 15;63(4):045014. Epub 2018 Feb 15.

Department of Electrical and Computer Engineering, Illinois Institute of Technology, Chicago, IL 60616, United States of America.

In computer-aided detection or diagnosis of clustered microcalcifications (MCs) in mammograms, the performance often suffers from not only the presence of false positives (FPs) among the detected individual MCs but also large variability in detection accuracy among different cases. To address this issue, we investigate a locally adaptive decision scheme in MC detection by exploiting the noise characteristics in a lesion area. Instead of developing a new MC detector, we propose a decision scheme on how to best decide whether a detected object is an MC or not in the detector output. Read More

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http://dx.doi.org/10.1088/1361-6560/aaaa4cDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5987532PMC
February 2018
1 Read

Re: "Computer-Aided Detection Can Bridge the Skill Gap in Multiple Sclerosis Monitoring".

J Am Coll Radiol 2018 01;15(1 Pt A):7-8

Neuroradiology Department, C. Mondino National Neurological Institute, IRCCS, Pavia, Italy; Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.

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https://linkinghub.elsevier.com/retrieve/pii/S15461440173110
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http://dx.doi.org/10.1016/j.jacr.2017.08.048DOI Listing
January 2018
4 Reads

Computer-aided diagnosis of contrast-enhanced spectral mammography: A feasibility study.

Eur J Radiol 2018 Jan 5;98:207-213. Epub 2017 Dec 5.

Mayo Clinic, Scottsdale, Dept. of Research.

Objective: To evaluate whether the use of a computer-aided diagnosis-contrast-enhanced spectral mammography (CAD-CESM) tool can further increase the diagnostic performance of CESM compared with that of experienced radiologists.

Materials And Methods: This IRB-approved retrospective study analyzed 50 lesions described on CESM from August 2014 to December 2015. Histopathologic analyses, used as the criterion standard, revealed 24 benign and 26 malignant lesions. Read More

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http://dx.doi.org/10.1016/j.ejrad.2017.11.024DOI Listing
January 2018
13 Reads

A curated mammography data set for use in computer-aided detection and diagnosis research.

Sci Data 2017 12 19;4:170177. Epub 2017 Dec 19.

Department of Radiology and Medicine (Biomedical Informatics Research), Stanford University, Stanford, CA 94305, USA.

Published research results are difficult to replicate due to the lack of a standard evaluation data set in the area of decision support systems in mammography; most computer-aided diagnosis (CADx) and detection (CADe) algorithms for breast cancer in mammography are evaluated on private data sets or on unspecified subsets of public databases. This causes an inability to directly compare the performance of methods or to replicate prior results. We seek to resolve this substantial challenge by releasing an updated and standardized version of the Digital Database for Screening Mammography (DDSM) for evaluation of future CADx and CADe systems (sometimes referred to generally as CAD) research in mammography. Read More

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http://dx.doi.org/10.1038/sdata.2017.177DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5735920PMC
December 2017
10 Reads

Prediction of breast cancer risk using a machine learning approach embedded with a locality preserving projection algorithm.

Phys Med Biol 2018 01 30;63(3):035020. Epub 2018 Jan 30.

School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, United States of America. Author to whom any correspondence should be addressed.

In order to automatically identify a set of effective mammographic image features and build an optimal breast cancer risk stratification model, this study aims to investigate advantages of applying a machine learning approach embedded with a locally preserving projection (LPP) based feature combination and regeneration algorithm to predict short-term breast cancer risk. A dataset involving negative mammograms acquired from 500 women was assembled. This dataset was divided into two age-matched classes of 250 high risk cases in which cancer was detected in the next subsequent mammography screening and 250 low risk cases, which remained negative. Read More

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http://stacks.iop.org/0031-9155/63/i=3/a=035020?key=crossref
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http://dx.doi.org/10.1088/1361-6560/aaa1caDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5801007PMC
January 2018
37 Reads

An Investigation of Bayes Algorithm and Neural Networks for Identifying the Breast Cancer.

Indian J Med Paediatr Oncol 2017 Jul-Sep;38(3):340-344

Department of ECE, PSG Institute of Technology and Applied Research, Coimbatore, Tamil Nadu, India.

Context: Breast cancer is a biggest threat to women. X-ray mammography is the most effective method for early detection and screening of breast cancer. It is a tough challenge for the radiologist in reading mammography since it does not provide consistent result every time. Read More

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http://dx.doi.org/10.4103/ijmpo.ijmpo_127_17DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5686979PMC
December 2017
2 Reads

Improving digital breast tomosynthesis reading time: A pilot multi-reader, multi-case study using concurrent Computer-Aided Detection (CAD).

Eur J Radiol 2017 Dec 24;97:83-89. Epub 2017 Oct 24.

Centre d'Imagerie Medicale Italie, 6 place d'Italie, 75013 Paris, France.

Purpose: Evaluate concurrent Computer-Aided Detection (CAD) with Digital Breast Tomosynthesis (DBT) to determine impact on radiologist performance and reading time.

Materials And Methods: The CAD system detects and extracts suspicious masses, architectural distortions and asymmetries from DBT planes that are blended into corresponding synthetic images to form CAD-enhanced synthetic images. Review of CAD-enhanced images and navigation to corresponding planes to confirm or dismiss potential lesions allows radiologists to more quickly review DBT planes. Read More

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http://dx.doi.org/10.1016/j.ejrad.2017.10.014DOI Listing
December 2017
5 Reads

A novel classification scheme to decline the mortality rate among women due to breast tumor.

Microsc Res Tech 2018 Feb 16;81(2):171-180. Epub 2017 Nov 16.

College of Computer and Information Sciences, Prince Sultan University, Riyadh, 11586, Saudi Arabia.

Early screening of skeptical masses or breast carcinomas in mammograms is supposed to decline the mortality rate among women. This amount can be decreased more on development of the computer-aided diagnosis with reduction of false suppositions in medical informatics. Our aim is to provide a robust tumor detection system for accurate classification of breast masses using normal, abnormal, benign, or malignant classes. Read More

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http://dx.doi.org/10.1002/jemt.22961DOI Listing
February 2018

Ultrasound Imaging Technologies for Breast Cancer Detection and Management: A Review.

Ultrasound Med Biol 2018 Jan 26;44(1):37-70. Epub 2017 Oct 26.

Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia, USA; The Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, Georgia, USA; Department of Mathematics and Computer Science, Emory College of Emory University, Atlanta, Georgia, USA; Winship Cancer Institute of Emory University, Atlanta, Georgia, USA. Electronic address:

Ultrasound imaging is a commonly used modality for breast cancer detection and diagnosis. In this review, we summarize ultrasound imaging technologies and their clinical applications for the management of breast cancer patients. The technologies include ultrasound elastography, contrast-enhanced ultrasound, 3-D ultrasound, automatic breast ultrasound and computer-aided detection of breast ultrasound. Read More

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http://dx.doi.org/10.1016/j.ultrasmedbio.2017.09.012DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6169997PMC
January 2018
25 Reads

Effectiveness and cost-effectiveness of double reading in digital mammography screening: A systematic review and meta-analysis.

Eur J Radiol 2017 Nov 21;96:40-49. Epub 2017 Sep 21.

Department of Clinical Epidemiology and Public Health, Hospital de la Santa Creu i Sant Pau (IIB Sant Pau), Barcelona, Spain; Universitat Autònoma de Barcelona (UAB), Barcelona, Spain; Iberoamerican Cochrane Centre, Barcelona, Spain; CIBER of Epidemiology and Public Health (CIBERESP), Spain. Electronic address:

Purpose: Double reading is the strategy of choice for mammogram interpretation in screening programmes. It remains, however, unknown whether double reading is still the strategy of choice in the context of digital mammography. Our aim was to determine the effectiveness and cost-effectiveness of double reading versus single reading of digital mammograms in screening programmes. Read More

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http://dx.doi.org/10.1016/j.ejrad.2017.09.013DOI Listing
November 2017
16 Reads

Detection and Segmentation of Pectoral Muscle on MLO-View Mammogram Using Enhancement Filter.

Authors:
P S Vikhe V R Thool

J Med Syst 2017 Oct 25;41(12):190. Epub 2017 Oct 25.

S.G. G. S. I of E & T, Nanded (MS), India.

The presence of predominant density region of the pectoral muscle in Medio-Lateral Oblique (MLO) view of the mammograms can affect or bias the results of mammograms processing for breast cancer detection using intensity based methods. Therefore, to improve the diagnostic performance of breast cancer detection using computer-aided system, identification and segmentation of pectoral muscle is an important task. This paper presents, an intensity based approach to identify the pectoral region in mammograms. Read More

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http://dx.doi.org/10.1007/s10916-017-0839-8DOI Listing
October 2017
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Concurrent Computer-Aided Detection Improves Reading Time of Digital Breast Tomosynthesis and Maintains Interpretation Performance in a Multireader Multicase Study.

AJR Am J Roentgenol 2018 Mar 24;210(3):685-694. Epub 2017 Oct 24.

2 Biostatistics Consulting, LLC, Kensington, MD.

Objective: Digital breast tomosynthesis (DBT) is more accurate than full-field digital mammography alone but requires a longer reading time. A radiologist reader study evaluated the use of concurrent computer-aided detection (CAD) to shorten the reading time while maintaining interpretation performance.

Materials And Methods: A CAD system was developed to detect suspicious soft-tissue densities in DBT planes. Read More

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https://www.ajronline.org/doi/10.2214/AJR.17.18185
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http://dx.doi.org/10.2214/AJR.17.18185DOI Listing
March 2018
8 Reads

Detection and classification of the breast abnormalities in digital mammograms via regional Convolutional Neural Network.

Conf Proc IEEE Eng Med Biol Soc 2017 07;2017:1230-1233

Automatic detection and classification of the masses in mammograms are still a big challenge and play a crucial role to assist radiologists for accurate diagnosis. In this paper, we propose a novel computer-aided diagnose (CAD) system based on one of the regional deep learning techniques: a ROI-based Convolutional Neural Network (CNN) which is called You Only Look Once (YOLO). Our proposed YOLO-based CAD system contains four main stages: mammograms preprocessing, feature extraction utilizing multi convolutional deep layers, mass detection with confidence model, and finally mass classification using fully connected neural network (FC-NN). Read More

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http://dx.doi.org/10.1109/EMBC.2017.8037053DOI Listing
July 2017
2 Reads

A cost-sensitive Bayesian combiner for reducing false positives in mammographic mass detection.

Biomed Tech (Berl) 2019 Feb;64(1):39-52

School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran.

Mammography is the most widely used modality for early breast cancer detection. This work proposes a new computer-aided mass detection approach, in which a denoising method called BM3D is first applied to mammograms. Afterwards, using an adaptive segmentation algorithm, images are segmented to suspicious regions of interest (ROIs) and then a classifier is used to understand the features of true positive (TP) and false positive (FP) patterns. Read More

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http://dx.doi.org/10.1515/bmt-2017-0032DOI Listing
February 2019
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The importance of early detection of calcifications associated with breast cancer in screening.

Breast Cancer Res Treat 2018 01 17;167(2):451-458. Epub 2017 Oct 17.

Department of Radiology and Nuclear Medicine, Radboud university medical center, Nijmegen, The Netherlands.

Purpose: The aim of this study was to assess how often women with undetected calcifications in prior screening mammograms are subsequently diagnosed with invasive cancer.

Methods: From a screening cohort of 63,895 women, exams were collected from 59,690 women without any abnormalities, 744 women with a screen-detected cancer and a prior negative exam, 781 women with a false positive exam based on calcifications, and 413 women with an interval cancer. A radiologist identified cancer-related calcifications, selected by a computer-aided detection system, on mammograms taken prior to screen-detected or interval cancer diagnoses. Read More

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http://dx.doi.org/10.1007/s10549-017-4527-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5790861PMC
January 2018
1 Read

Classifying symmetrical differences and temporal change for the detection of malignant masses in mammography using deep neural networks.

J Med Imaging (Bellingham) 2017 Oct 10;4(4):044501. Epub 2017 Oct 10.

RadboudUMC Nijmegen, Diagnostic Image Analysis Group, Department of Radiology and Nuclear Medicine, Nijmegen, The Netherlands.

We investigate the addition of symmetry and temporal context information to a deep convolutional neural network (CNN) with the purpose of detecting malignant soft tissue lesions in mammography. We employ a simple linear mapping that takes the location of a mass candidate and maps it to either the contralateral or prior mammogram, and regions of interest (ROIs) are extracted around each location. Two different architectures are subsequently explored: (1) a fusion model employing two datastreams where both ROIs are fed to the network during training and testing and (2) a stagewise approach where a single ROI CNN is trained on the primary image and subsequently used as a feature extractor for both primary and contralateral or prior ROIs. Read More

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http://dx.doi.org/10.1117/1.JMI.4.4.044501DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5633751PMC
October 2017
12 Reads