697 results match your criteria Mammography - Computer-Aided Detection


A deep learning-based automated diagnostic system for classifying mammographic lesions.

Medicine (Baltimore) 2020 Jul;99(27):e20977

Division of Breast and Medical Oncology, National Cancer Center Hospital East, Chiba, Japan.

Background: Screening mammography has led to reduced breast cancer-specific mortality and is recommended worldwide. However, the resultant doctors' workload of reading mammographic scans needs to be addressed. Although computer-aided detection (CAD) systems have been developed to support readers, the findings are conflicting regarding whether traditional CAD systems improve reading performance. Read More

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http://dx.doi.org/10.1097/MD.0000000000020977DOI Listing

Staying abreast of imaging - Current status of breast cancer detection in high density breast.

Radiography (Lond) 2020 Jun 28. Epub 2020 Jun 28.

Diagnostic Radiology Department, American University of Beirut Medical Center, P.O.Box: 11-0236, Riad El-Solh, Beirut, 1107 2020, Lebanon. Electronic address:

Objectives: The aim of this paper is to illustrate the current status of imaging in high breast density as we enter a new decade of advancing medicine and technology to diagnose breast lesions.

Key Findings: Early detection of breast cancer has become the chief focus of research from governments to individuals. However, with varying breast densities across the globe, the explosion of breast density information related to imaging, phenotypes, diet, computer aided diagnosis and artificial intelligence has witnessed a dramatic shift in new screening recommendations in mammography, physical examination, screening younger women and women with comorbid conditions, screening women at high risk, and new screening technologies. Read More

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http://dx.doi.org/10.1016/j.radi.2020.06.003DOI Listing

Artificial intelligence for breast cancer detection in mammography and digital breast tomosynthesis: State of the art.

Semin Cancer Biol 2020 Jun 9. Epub 2020 Jun 9.

Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein 10, 6525 GA, Nijmegen, the Netherlands; Department of Radiology, Netherlands Cancer Institute (NKI), Plesmanlaan 121, 1066 CX, Amsterdam, the Netherlands. Electronic address:

Screening for breast cancer with mammography has been introduced in various countries over the last 30 years, initially using analog screen-film-based systems and, over the last 20 years, transitioning to the use of fully digital systems. With the introduction of digitization, the computer interpretation of images has been a subject of intense interest, resulting in the introduction of computer-aided detection (CADe) and diagnosis (CADx) algorithms in the early 2000's. Although they were introduced with high expectations, the potential improvement in the clinical realm failed to materialize, mostly due to the high number of false positive marks per analyzed image. Read More

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http://dx.doi.org/10.1016/j.semcancer.2020.06.002DOI Listing

Worse in real life: An eye-tracking examination of the cost of CAD at low prevalence.

J Exp Psychol Appl 2020 May 7. Epub 2020 May 7.

Department of Psychology, University of Utah.

Computer-aided detection (CAD) is applied during screening mammography for millions of women each year. Despite its popularity, several large studies have observed no benefit in breast cancer detection for practices that use CAD. This lack of benefit may be driven by how CAD information is conveyed to the radiologist. Read More

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http://dx.doi.org/10.1037/xap0000277DOI Listing

Segmentation of Masses on Mammograms Using Data Augmentation and Deep Learning.

J Digit Imaging 2020 Mar 23. Epub 2020 Mar 23.

Software Innovation Laboratory - SOFTWARELAB, Applied Computing Graduate Program, Universidade do Vale do Rio dos Sinos - Unisinos, Av. Unisinos 950, São Leopoldo, 93022-000, Brazil.

The diagnosis of breast cancer in early stage is essential for successful treatment. Detection can be performed in several ways, the most common being through mammograms. The projections acquired by this type of examination are directly affected by the composition of the breast, which density can be similar to the suspicious masses, being a challenge the identification of malignant lesions. Read More

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http://dx.doi.org/10.1007/s10278-020-00330-4DOI Listing

Interpretation time for screening mammography as a function of the number of computer-aided detection marks.

J Med Imaging (Bellingham) 2020 Mar 3;7(2):022408. Epub 2020 Feb 3.

University of Texas MD Anderson Cancer Center, Houston, Texas, United States.

Computer-aided detection (CAD) alerts radiologists to findings potentially associated with breast cancer but is notorious for creating false-positive marks. Although a previous paper found that radiologists took more time to interpret mammograms with more CAD marks, our impression was that this was not true in actual interpretation. We hypothesized that radiologists would selectively disregard these marks when present in larger numbers. Read More

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http://dx.doi.org/10.1117/1.JMI.7.2.022408DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6996587PMC

Deep Learning Computer-Aided Diagnosis for Breast Lesion in Digital Mammogram.

Adv Exp Med Biol 2020 ;1213:59-72

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

For computer-aided diagnosis (CAD), detection, segmentation, and classification from medical imagery are three key components to efficiently assist physicians for accurate diagnosis. In this chapter, a completely integrated CAD system based on deep learning is presented to diagnose breast lesions from digital X-ray mammograms involving detection, segmentation, and classification. To automatically detect breast lesions from mammograms, a regional deep learning approach called You-Only-Look-Once (YOLO) is used. Read More

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http://dx.doi.org/10.1007/978-3-030-33128-3_4DOI Listing
February 2020

Computer-Aided Histopathological Image Analysis Techniques for Automated Nuclear Atypia Scoring of Breast Cancer: a Review.

J Digit Imaging 2020 Jan 27. Epub 2020 Jan 27.

Artificial Intelligence & Computer Vision Lab, Department of Computer Science, Cochin University of Science and Technology, Kochi, Kerala, 682022, India.

Breast cancer is the most common type of malignancy diagnosed in women. Through early detection and diagnosis, there is a great chance of recovery and thereby reduce the mortality rate. Many preliminary tests like non-invasive radiological diagnosis using ultrasound, mammography, and MRI are widely used for the diagnosis of breast cancer. Read More

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http://dx.doi.org/10.1007/s10278-019-00295-zDOI Listing
January 2020

Efficient Denoising Framework for Mammogram Images with a New Impulse Detector and Non-Local Means.

Asian Pac J Cancer Prev 2020 Jan 1;21(1):179-183. Epub 2020 Jan 1.

Department of Electronics and Communication Engineering, Bannari Amman Institute of Technology, India.

Objective: The survival rates of breast cancer are increasing as screening and diagnosis improve. The removal of noise is revealed to be a significant step for automatic - computer aided detection (CAD) of microcalcification in digital mammography.

Methods: In this paper, a combined approach for eradicating impulse noise from digital mammograms is proposed. Read More

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http://dx.doi.org/10.31557/APJCP.2020.21.1.179DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7294012PMC
January 2020

Computer Aided Detection of Clustered Microcalcification: A Survey.

Curr Med Imaging Rev 2019 ;15(2):132-149

Department of Electronics and Communication Engineering, PES College of Engineering, Mandya, Karnataka, India.

Background: This paper attempts to pinpoint different techniques for Pectoral Muscle (PM) segmentation, Microcalcification (MC) detection and classification in digital mammograms. The segmentation of PM and detection of MC and its classification are mostly based on image processing and data mining techniques.

Discussion: The review centered on major techniques in image processing and data mining that is employed for PM segmentation, MC detection and classification in digital mammograms. Read More

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http://dx.doi.org/10.2174/1573405614666181012103750DOI Listing
January 2019

Stand-alone artificial intelligence - The future of breast cancer screening?

Breast 2020 Feb 2;49:254-260. Epub 2020 Jan 2.

Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Geert Grooteplein 10, 6525 GA, Nijmegen, the Netherlands; Department of Radiology, Netherlands Cancer Institute (NKI), Plesmanlaan 121, 1066 CX, Amsterdam, the Netherlands. Electronic address:

Although computers have had a role in interpretation of mammograms for at least two decades, their impact on performance has not lived up to expectations. However, in the last five years, the field of medical image analysis has undergone a revolution due to the introduction of deep learning convolutional neural networks - a form of artificial intelligence (AI). Because of their considerably higher performance compared to conventional computer aided detection methods, these AI algorithms have resulted in renewed interest in their potential for interpreting breast images in stand-alone mode. Read More

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http://dx.doi.org/10.1016/j.breast.2019.12.014DOI Listing
February 2020

Is there a safety-net effect with computer-aided detection?

J Med Imaging (Bellingham) 2020 Mar 26;7(2):022405. Epub 2019 Dec 26.

Division of Neuroscience and Experimental Psychology, University of Manchester, Faculty of Biology, Medicine and Health, Manchester, United Kingdom.

Computer-aided detection (CAD) systems are used to aid readers interpreting screening mammograms. An expert reader searches the image initially unaided and then once again with the aid of CAD, which prompts automatically detected suspicious regions. This could lead to a "safety-net" effect, where the initial unaided search of the image is adversely affected by the fact that it is preliminary to an additional search with CAD and may, therefore, be less thorough. Read More

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http://dx.doi.org/10.1117/1.JMI.7.2.022405DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6931663PMC

Breast cancer diagnosis using thermography and convolutional neural networks.

Med Hypotheses 2020 Apr 27;137:109542. Epub 2019 Dec 27.

Department of Software Engineering, Firat University, 23119 Elazig, Turkey.

Thermography is an entirely non-invasive and non-contact imaging technique that is widely used in the medicinal field. Since the early detection of cancer is very important, the computer-aided system can increase the rate of diagnosis, cure, and survival of the affected person. Considering the high cost of treatment in addition to the high prevalence of affected persons, early diagnosis is the most important step in reducing the health and social complications of this disease. Read More

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http://dx.doi.org/10.1016/j.mehy.2019.109542DOI Listing

Classification of Mammogram Images Using Multiscale all Convolutional Neural Network (MA-CNN).

J Med Syst 2019 Dec 14;44(1):30. Epub 2019 Dec 14.

Department of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India.

Breast cancer is one of the leading causes of cancer death among women in worldwide. Early diagnosis of breast cancer improves the chance of survival by aiding proper clinical treatments. The digital mammography examination helps in diagnosing the breast cancer at its earlier stage. Read More

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http://dx.doi.org/10.1007/s10916-019-1494-zDOI Listing
December 2019

[Artificial intelligence in the diagnosis of breast cancer : Yesterday, today and tomorrow].

Radiologe 2020 Jan;60(1):56-63

Universitätsklinik für Radiologie und Nuklearmedizin, Medizinische Universität Wien, Währinger Gürtel 18-20, 1090, Wien, Österreich.

Background: Artificial intelligence (AI) is increasingly applied in the field of breast imaging.

Objectives: What are the main areas where AI is applied in breast imaging and what AI and computer-aided diagnosis (CAD) systems are already available?

Materials And Methods: Basic literature and vendor-supplied information are screened for relevant information, which is then pooled, structured and discussed from the perspective of breast imaging.

Results: Original CAD systems in mammography date almost 25 years back. Read More

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http://dx.doi.org/10.1007/s00117-019-00615-yDOI Listing
January 2020

Evaluation of data augmentation via synthetic images for improved breast mass detection on mammograms using deep learning.

J Med Imaging (Bellingham) 2020 Jan 22;7(1):012703. Epub 2019 Nov 22.

U.S. Food and Drug Administration, Silver Spring, Maryland, United States.

We evaluated whether using synthetic mammograms for training data augmentation may reduce the effects of overfitting and increase the performance of a deep learning algorithm for breast mass detection. Synthetic mammograms were generated using procedural analytic breast and breast mass modeling algorithms followed by simulated x-ray projections of the breast models into mammographic images. breast phantoms containing masses were modeled across the four BI-RADS breast density categories, and the masses were modeled with different sizes, shapes, and margins. Read More

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http://dx.doi.org/10.1117/1.JMI.7.1.012703DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6872953PMC
January 2020

CAD and AI for breast cancer-recent development and challenges.

Br J Radiol 2020 Apr 16;93(1108):20190580. Epub 2019 Dec 16.

Department of Radiology, University of Michigan, Ann Arbor, MI, United States.

Computer-aided diagnosis (CAD) has been a popular area of research and development in the past few decades. In CAD, machine learning methods and multidisciplinary knowledge and techniques are used to analyze the patient information and the results can be used to assist clinicians in their decision making process. CAD may analyze imaging information alone or in combination with other clinical data. Read More

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http://dx.doi.org/10.1259/bjr.20190580DOI Listing

Breast Cancer Diagnosis Using an Efficient CAD System Based on Multiple Classifiers.

Diagnostics (Basel) 2019 Oct 26;9(4). Epub 2019 Oct 26.

Electronics and Communications Engineering Department, Arab Academy for Science, Technology, and Maritime Transport (AASTMT), Alexandria 1029, Egypt.

Breast cancer is one of the major health issues across the world. In this study, a new computer-aided detection (CAD) system is introduced. First, the mammogram images were enhanced to increase the contrast. Read More

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http://dx.doi.org/10.3390/diagnostics9040165DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6963468PMC
October 2019
4 Reads

Utilization and Cost of Mammography Screening Among Commercially Insured Women 50 to 64 Years of Age in the United States, 2012-2016.

J Womens Health (Larchmt) 2020 Mar 15;29(3):327-337. Epub 2019 Oct 15.

Division of Cancer Prevention and Control, Centers for Disease Control and Prevention, Atlanta, Georgia.

In recent years, most insurance plans eliminated cost-sharing for breast cancer screening and recommended screening intervals changed, and newer modalities-digital mammography and breast tomosynthesis-became more widely available. The objectives of this study are to examine how these changes affected utilization, frequency, and costs of breast cancer screening among commercially insured women, and to understand factors associated with utilization and frequency of screening. This study used commercial insurance claims data for women 50 to 64 years of age, continuously enrolled in commercial insurance plans during 2012-2016. Read More

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http://dx.doi.org/10.1089/jwh.2018.7543DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7082198PMC
March 2020
1 Read

Systematic reviews as a "lens of evidence": Determinants of cost-effectiveness of breast cancer screening.

Cancer Med 2019 12 30;8(18):7846-7858. Epub 2019 Sep 30.

The section of Early Detection and Prevention, International Agency for Research on Cancer, Lyon, France.

Systematic reviews with economic components are important decision tools for stakeholders seeking to evaluate technologies, such as breast cancer screening (BCS) programs. This overview of systematic reviews explores the determinants of the cost-effectiveness of BCS and assesses the quality of secondary evidence. The search identified 30 systematic reviews that reported on the determinants of the cost-effectiveness of BCS, including the costs of breast cancer and BCS. Read More

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http://dx.doi.org/10.1002/cam4.2498DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6912065PMC
December 2019
3 Reads

Artificial Intelligence for Mammography and Digital Breast Tomosynthesis: Current Concepts and Future Perspectives.

Radiology 2019 11 24;293(2):246-259. Epub 2019 Sep 24.

From the Center for Biomedical Imaging (K.J.G., L.M.), Center for Data Science (K.J.G.), Center for Advanced Imaging Innovation and Research (L.M.), and Laura and Isaac Perlmutter Cancer Center (L.M.), New York University School of Medicine, 160 E 34th St, 3rd Floor, New York, NY 10016; Department of Radiology and Nuclear Medicine, Radboud University Medical Centre, Nijmegen, the Netherlands (R.M.M.); and Department of Radiology, the Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, Amsterdam, the Netherlands (R.M.M.).

Although computer-aided diagnosis (CAD) is widely used in mammography, conventional CAD programs that use prompts to indicate potential cancers on the mammograms have not led to an improvement in diagnostic accuracy. Because of the advances in machine learning, especially with use of deep (multilayered) convolutional neural networks, artificial intelligence has undergone a transformation that has improved the quality of the predictions of the models. Recently, such deep learning algorithms have been applied to mammography and digital breast tomosynthesis (DBT). Read More

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http://dx.doi.org/10.1148/radiol.2019182627DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6822772PMC
November 2019
2 Reads

A survey of breast cancer screening techniques: thermography and electrical impedance tomography.

J Med Eng Technol 2019 Jul 23;43(5):305-322. Epub 2019 Sep 23.

FEMTO-ST Institute, University Bourgogne Franche-Comté, CNRS, ENSMM , Besançon , France.

Breast cancer is a disease that threat many women's life, thus, the early and accurate detection play a key role in reducing the mortality rate. Mammography stands as the reference technique for breast cancer screening; nevertheless, many countries still lack access to mammograms due to economic, social and cultural issues. Last advances in computational tools, infra-red cameras and devices for bio-impedance quantification allowed the development of parallel techniques like, thermography, infra-red imaging and electrical impedance tomography, these being faster, reliable and cheaper. Read More

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http://dx.doi.org/10.1080/03091902.2019.1664672DOI Listing
July 2019
2 Reads

Role of image thermography in early breast cancer detection- Past, present and future.

Comput Methods Programs Biomed 2020 Jan 7;183:105074. Epub 2019 Sep 7.

Department of Electronics & Communication Engineering, Indian Institute of Information Technology Allahabad, Prayagraj, Uttar Pradesh, India. Electronic address:

One of the most prevalent cancers among women is the breast cancer. Accurate diagnosis of breast cancer at an early stage can reduce the mortality associated with this disease. Infrared Breast Thermography, which is a screening tool used to measure the temperature distribution of breast tissue, is a suitable adjunct tool to mammography. Read More

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http://dx.doi.org/10.1016/j.cmpb.2019.105074DOI Listing
January 2020
2 Reads

Microcalcification detection in full-field digital mammograms: A fully automated computer-aided system.

Phys Med 2019 Aug 14;64:1-9. Epub 2019 Jun 14.

I.R.C.C.S. "Giovanni Paolo II" National Cancer Centre, Bari, Italy.

Background: Microcalcification clusters in mammograms can be considered as early signs of breast cancer. However, their detection is a very challenging task because of different factors: large variety of breast composition, highly textured breast anatomy, impalpable size of microcalcifications in some cases, as well as inherent low contrast of mammograms. Thus, the need to support the clinicians' work with an automatic tool. Read More

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http://dx.doi.org/10.1016/j.ejmp.2019.05.022DOI Listing
August 2019
1 Read

An automated mammogram classification system using modified support vector machine.

Med Devices (Auckl) 2019 12;12:275-284. Epub 2019 Aug 12.

Department of Computer and Information Sciences, Covenant University, Ota, Ogun State, Nigeria.

Purpose: Breast cancer remains a serious public health problem that results in the loss of lives among women. However, early detection of its signs increases treatment options and the likelihood of cure. Although mammography has been established to be a proven technique of examining symptoms of cancer in mammograms, the manual observation by radiologists is demanding and often prone to diagnostic errors. Read More

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http://dx.doi.org/10.2147/MDER.S206973DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6697673PMC
August 2019
6 Reads

Breast Cancer Detection and Diagnosis Using Mammographic Data: Systematic Review.

J Med Internet Res 2019 07 26;21(7):e14464. Epub 2019 Jul 26.

National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong, Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.

Background: Machine learning (ML) has become a vital part of medical imaging research. ML methods have evolved over the years from manual seeded inputs to automatic initializations. The advancements in the field of ML have led to more intelligent and self-reliant computer-aided diagnosis (CAD) systems, as the learning ability of ML methods has been constantly improving. Read More

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http://dx.doi.org/10.2196/14464DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6688437PMC

Pulse pileup analysis for a double-sided silicon strip detector using variable pulse shapes.

IEEE Trans Nucl Sci 2019 Jun 15;66(6):960-968. Epub 2019 May 15.

R. Fahrig was with the Department of Radiology, Stanford University, Stanford, CA 94305 USA. She is now with Siemens Healthcare GmbH, Erlangen, 91052 Germany, also with Pattern Recognition Lab, Friedrich-Alexander-University, Erlangen-Nuremberg, 91052 Germany.

Due to pulse pileup, photon counting detectors (PCDs) suffer from count loss and energy distortion when operating in high count rate environments. In this paper, we studied the pulse pileup of a double-sided silicon strip detector (DSSSD) to evaluate its potential application in a mammography system. We analyzed the pulse pileup using pulses of varied shapes, where the shape of the pulse depends on the location of photon interaction within the detector. Read More

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http://dx.doi.org/10.1109/TNS.2019.2917144DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6640861PMC
June 2019
2 Reads

Computer-Aided Detection for Breast Cancer Screening in Clinical Settings: Scoping Review.

JMIR Med Inform 2019 Jul 18;7(3):e12660. Epub 2019 Jul 18.

Health Information Research Unit, Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada.

Background: With the growth of machine learning applications, the practice of medicine is evolving. Computer-aided detection (CAD) is a software technology that has become widespread in radiology practices, particularly in breast cancer screening for improving detection rates at earlier stages. Many studies have investigated the diagnostic accuracy of CAD, but its implementation in clinical settings has been largely overlooked. Read More

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http://dx.doi.org/10.2196/12660DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6670274PMC
July 2019
8 Reads

Breast pectoral muscle segmentation in mammograms using a modified holistically-nested edge detection network.

Med Image Anal 2019 10 20;57:1-17. Epub 2019 Jun 20.

Vicomtech Foundation, San Sebastián, Spain.

This paper presents a method for automatic breast pectoral muscle segmentation in mediolateral oblique mammograms using a Convolutional Neural Network (CNN) inspired by the Holistically-nested Edge Detection (HED) network. Most of the existing methods in the literature are based on hand-crafted models such as straight-line, curve-based techniques or a combination of both. Unfortunately, such models are insufficient when dealing with complex shape variations of the pectoral muscle boundary and when the boundary is unclear due to overlapping breast tissue. Read More

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http://dx.doi.org/10.1016/j.media.2019.06.007DOI Listing
October 2019
8 Reads

Does Computer-aided Detection Help in Interpretation of Automated Breast US?

Radiology 2019 Sep 18;292(3):550-551. Epub 2019 Jun 18.

From the Department of Radiology (FGH-3), Boston University Medical Center, 830 Harrison Avenue, Boston, MA 02118.

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http://dx.doi.org/10.1148/radiol.2019191107DOI Listing
September 2019
6 Reads

Deep convolutional neural networks for mammography: advances, challenges and applications.

BMC Bioinformatics 2019 Jun 6;20(Suppl 11):281. Epub 2019 Jun 6.

Department of Computer Science and Engineering, University of Connecticut, Storrs, 06269, CT, USA.

Background: The limitations of traditional computer-aided detection (CAD) systems for mammography, the extreme importance of early detection of breast cancer and the high impact of the false diagnosis of patients drive researchers to investigate deep learning (DL) methods for mammograms (MGs). Recent breakthroughs in DL, in particular, convolutional neural networks (CNNs) have achieved remarkable advances in the medical fields. Specifically, CNNs are used in mammography for lesion localization and detection, risk assessment, image retrieval, and classification tasks. Read More

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http://dx.doi.org/10.1186/s12859-019-2823-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6551243PMC
June 2019
25 Reads

A Hybridized ELM for Automatic Micro Calcification Detection in Mammogram Images Based on Multi-Scale Features.

J Med Syst 2019 May 15;43(7):183. Epub 2019 May 15.

Department of Electronics and Communication Engineering, Toc H Institute of Science and Technology, Ernakulam, Kerala, India.

Detection of masses and micro calcifications are a stimulating task for radiologists in digital mammogram images. Radiologists using Computer Aided Detection (CAD) frameworks to find the breast lesion. Micro calcification may be the early sign of breast cancer. Read More

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http://dx.doi.org/10.1007/s10916-019-1316-3DOI Listing
May 2019
9 Reads

Adaptive hysteresis thresholding segmentation technique for localizing the breast masses in the curve stitching domain.

Int J Med Inform 2019 06 28;126:26-34. Epub 2019 Feb 28.

Department of Computer Science, COMSATS University Islamabad, Wah Campus 47040, Pakistan.

Background And Objective: Massive work by distinguished researchers in the domain of breast segmentation has been proposed. However, no significant solution reduces the limitations of the false positive rate of cancerous cells in the breast body for probing the abnormalities of particular features. This problem is challenging in its nature and essential to be solved. Read More

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http://dx.doi.org/10.1016/j.ijmedinf.2019.02.001DOI Listing

Improved Cancer Detection Using Artificial Intelligence: a Retrospective Evaluation of Missed Cancers on Mammography.

J Digit Imaging 2019 08;32(4):625-637

Memorial Sloan Kettering Cancer Center, 300 East 66th Street, New York, NY, 10065, USA.

To determine whether cmAssist™, an artificial intelligence-based computer-aided detection (AI-CAD) algorithm, can be used to improve radiologists' sensitivity in breast cancer screening and detection. A blinded retrospective study was performed with a panel of seven radiologists using a cancer-enriched data set from 122 patients that included 90 false-negative mammograms obtained up to 5.8 years prior to diagnosis and 32 BIRADS 1 and 2 patients with a 2-year follow-up of negative diagnosis. Read More

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http://dx.doi.org/10.1007/s10278-019-00192-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6646649PMC
August 2019
12 Reads

Reduction of False-Positive Markings on Mammograms: a Retrospective Comparison Study Using an Artificial Intelligence-Based CAD.

J Digit Imaging 2019 08;32(4):618-624

Voxel Imaging, Inc., 2711 N. Sepulveda Blvd., #284, Manhattan Beach, CA, 90266, USA.

The aim was to determine whether an artificial intelligence (AI)-based, computer-aided detection (CAD) software can be used to reduce false positive per image (FPPI) on mammograms as compared to an FDA-approved conventional CAD. A retrospective study was performed on a set of 250 full-field digital mammograms between January 1, 2013, and March 31, 2013, and the number of marked regions of interest of two different systems was compared for sensitivity and specificity in cancer detection. The count of false-positive marks per image (FPPI) of the two systems was also evaluated as well as the number of cases that were completely mark-free. Read More

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http://dx.doi.org/10.1007/s10278-018-0168-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6646646PMC
August 2019
7 Reads

Three-dimensional automated breast ultrasound: Technical aspects and first results.

Authors:
A Vourtsis

Diagn Interv Imaging 2019 Oct 5;100(10):579-592. Epub 2019 Apr 5.

"Diagnostic Mammography" Medical Diagnostic Imaging Unit, Kifisias Avenue 362, Chalandri, 15233 Athens, Greece. Electronic address:

Three-dimensional automated breast ultrasound system (3D ABUS) is an innovation in breast ultrasound that has been developed to uncouple detection from image acquisition and to address the limitations of handheld ultrasound (HHUS). 3D ABUS provides a large field of view using high frequency transducers, producing high-resolution images and covering a large portion of the breast with one sweep. As more data become available on breast density and the impact of supplemental screening, 3D ABUS has gained wider acceptance as an adjunct tool to mammography. Read More

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https://linkinghub.elsevier.com/retrieve/pii/S22115684193007
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http://dx.doi.org/10.1016/j.diii.2019.03.012DOI Listing
October 2019
9 Reads

Breast cancer detection using synthetic mammograms from generative adversarial networks in convolutional neural networks.

J Med Imaging (Bellingham) 2019 Jul 23;6(3):031411. Epub 2019 Mar 23.

George Washington University, Medical Imaging and Image Analysis Laboratory, Department of Biomedical Engineering, Washington DC, United States.

The convolutional neural network (CNN) is a promising technique to detect breast cancer based on mammograms. Training the CNN from scratch, however, requires a large amount of labeled data. Such a requirement usually is infeasible for some kinds of medical image data such as mammographic tumor images. Read More

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http://dx.doi.org/10.1117/1.JMI.6.3.031411DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6430964PMC
July 2019
3 Reads

Artificial intelligence in breast imaging.

Clin Radiol 2019 05 18;74(5):357-366. Epub 2019 Mar 18.

EPSRC Centre for Mathematical and Statistical Analysis of Multimodal Clinical Imaging, University of Cambridge, Cambridge CB3 0WA, UK; Department of Radiology, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Hills Road, Cambridge CB2 0QQ, UK. Electronic address:

This article reviews current limitations and future opportunities for the application of computer-aided detection (CAD) systems and artificial intelligence in breast imaging. Traditional CAD systems in mammography screening have followed a rules-based approach, incorporating domain knowledge into hand-crafted features before using classical machine learning techniques as a classifier. The first commercial CAD system, ImageChecker M1000, relies on computer vision techniques for pattern recognition. Read More

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http://dx.doi.org/10.1016/j.crad.2019.02.006DOI Listing
May 2019
5 Reads

Breast mass detection and diagnosis using fused features with density.

J Xray Sci Technol 2019 ;27(2):321-342

College of Engineering, University of Texas at El Paso, USA.

Background: The morbidity of breast cancer has been increased in these years and ranked the first of all female diseases. Computer-aided diagnosis techniques for mammograms can help radiologists find early breast lesions. In mammograms, the degree of malignancy of the tumor is not only related to its morphology and texture features, but also closely related to the density of the tumor. Read More

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https://www.medra.org/servlet/aliasResolver?alias=iospress&a
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http://dx.doi.org/10.3233/XST-180461DOI Listing
July 2020
16 Reads

Computational insertion of microcalcification clusters on mammograms: reader differentiation from native clusters and computer-aided detection comparison.

J Med Imaging (Bellingham) 2018 Oct 19;5(4):044502. Epub 2018 Nov 19.

U.S. Food and Drug Administration, Center for Devices and Radiological Health, Silver Spring, Maryland, United States.

Mammographic computer-aided detection (CADe) devices are typically first developed and assessed for a specific "original" acquisition system. When developers are ready to apply their CADe device to a mammographic acquisition system, they typically assess the device with images acquired using the system. Collecting large repositories of clinical images containing verified lesion locations acquired by a system is costly and time consuming. Read More

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http://dx.doi.org/10.1117/1.JMI.5.4.044502DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6241543PMC
October 2018
21 Reads

Detection of potential microcalcification clusters using multivendor for-presentation digital mammograms for short-term breast cancer risk estimation.

Med Phys 2019 Apr 7;46(4):1938-1946. Epub 2019 Mar 7.

Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, SE-17177, Sweden.

Purpose: We explore using the number of potential microcalcification clusters detected in for-presentation mammographic images (the images which are typically accessible to large epidemiological studies) a marker of short-term breast cancer risk.

Methods: We designed a three-step algorithm for detecting potential microcalcification clusters in for-presentation digital mammograms. We studied association with short-term breast cancer risk using a nested case control design, with a mammography screening cohort as a source population. Read More

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http://dx.doi.org/10.1002/mp.13450DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6850331PMC
April 2019
5 Reads

Digital Mammography versus Digital Mammography Plus Tomosynthesis in Breast Cancer Screening: The Oslo Tomosynthesis Screening Trial.

Radiology 2019 04 19;291(1):23-30. Epub 2019 Feb 19.

From the Division of Radiology and Nuclear Medicine, Oslo University Hospital, University of Oslo, Breast Imaging Center, PO Box 4950, Nydalen, 0424 Oslo, Norway (P.S., R.G.); Departments of Biostatistics (A.I.B.) and Radiology (D.G.), University of Pittsburgh, Pittsburgh, Pa; Retired, former employee of Hologic (L.T.N.); Department of Screening, Cancer Registry of Norway, Oslo, Norway (S.S., S.H.); Department of Diagnostic Physics, Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo, Norway (B.H.Ø.); and Department of Health Sciences, Oslo Metropolitan University, Oslo, Norway (S.H.).

Background Digital breast tomosynthesis (DBT) is replacing digital mammography (DM) in the clinical workflow. Currently, there are limited prospective studies comparing the diagnostic accuracy of both examinations and the role of synthetic mammography (SM) and computer-aided detection (CAD). Purpose To compare the accuracy of DM versus DM + DBT in population-based breast cancer screening. Read More

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http://dx.doi.org/10.1148/radiol.2019182394DOI Listing
April 2019
13 Reads

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

Methods 2019 08 13;166:103-111. 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
August 2019
8 Reads

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
3 Reads

[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
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6765570PMC
January 2019
10 Reads

Breast Cancer Detection using Crow Search Optimization based Intuitionistic Fuzzy Clustering with Neighborhood Attraction

Asian Pac J Cancer Prev 2019 Jan 25;20(1):157-165. Epub 2019 Jan 25.

Department of Computer Technology, Kongu Engineering College, Perundurai, Tamilnadu, India. Email:

Objective: Generally, medical images contain lots of noise that may lead to uncertainty in diagnosing the abnormalities. Computer aided diagnosis systems offer a support to the radiologists in identifying the disease affected area. In mammographic images, some normal tissues may appear to be similar to masses and it is tedious to differentiate them. Read More

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http://dx.doi.org/10.31557/APJCP.2019.20.1.157DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6485576PMC
January 2019
6 Reads
1.500 Impact Factor

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

AJR Am J Roentgenol 2019 02;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
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6927034PMC
February 2019
31 Reads

Developing global image feature analysis models to predict cancer risk and prognosis.

Vis Comput Ind Biomed Art 2019 19;2(1):17. Epub 2019 Nov 19.

School of Electrical and Computer Engineering, University of Oklahoma, 101 David L. Boren Blvd, Suite 1001, Norman, OK 73019 USA.

In order to develop precision or personalized medicine, identifying new quantitative imaging markers and building machine learning models to predict cancer risk and prognosis has been attracting broad research interest recently. Most of these research approaches use the similar concepts of the conventional computer-aided detection schemes of medical images, which include steps in detecting and segmenting suspicious regions or tumors, followed by training machine learning models based on the fusion of multiple image features computed from the segmented regions or tumors. However, due to the heterogeneity and boundary fuzziness of the suspicious regions or tumors, segmenting subtle regions is often difficult and unreliable. Read More

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http://dx.doi.org/10.1186/s42492-019-0026-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7055572PMC
November 2019

Decrease in interpretation time for both novice and experienced readers using a concurrent computer-aided detection system for digital breast tomosynthesis.

Eur Radiol 2019 May 13;29(5):2518-2525. Epub 2018 Dec 13.

Advanced Medical Device Research Division, Korea Electrotechnology Research Institute, 111 Hanggaul-ro, Sangnok-gu, Ansan-si, Gyeonggi-do, 15588, Republic of Korea.

Objectives: To compare the diagnostic performance and interpretation time of digital breast tomosynthesis (DBT) for both novice and experienced readers with and without using a computer-aided detection (CAD) system for concurrent read.

Methods: CAD system was developed for concurrent read in DBT interpretation. In this observer performance study, we used an enriched sample of 100 DBT cases including 70 with and 30 without breast cancers. Read More

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http://dx.doi.org/10.1007/s00330-018-5886-0DOI Listing
May 2019
27 Reads

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
13 Reads