728 results match your criteria Mammography - Computer-Aided Detection

Mammographic microcalcifications and risk of breast cancer.

Br J Cancer 2021 Jun 14. Epub 2021 Jun 14.

Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden.

Background: Mammographic microcalcifications are considered early signs of breast cancer (BC). We examined the association between microcalcification clusters and the risk of overall and subtype-specific BC. Furthermore, we studied how mammographic density (MD) influences the association between microcalcification clusters and BC risk. Read More

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Pattern classification for breast lesion on FFDM by integration of radiomics and deep features.

Comput Med Imaging Graph 2021 Jun 14;90:101922. Epub 2021 Apr 14.

Department of Radiation Oncology, Huizhou Municipal Central Hospital, Huizhou, Guangdong, 516001, China. Electronic address:

The radiomics model can be used in breast cancer detection via calculating quantitative image features. However, these features are explicitly designed, or handcrafted in advance, and this would limit their ability to characterize the lesion properly. This paper aims to build an integrated-features-based classification framework which cooperate the radiomics features and the deep features to classify benign and malignant breast lesions on full-filed digital mammography (FFDM). Read More

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Deep Learning-Based Artificial Intelligence for Mammography.

Korean J Radiol 2021 May 4. Epub 2021 May 4.

Department of Radiology, Yongin Severance Hospital, Yonsei University, College of Medicine, Yongin, Korea.

During the past decade, researchers have investigated the use of computer-aided mammography interpretation. With the application of deep learning technology, artificial intelligence (AI)-based algorithms for mammography have shown promising results in the quantitative assessment of parenchymal density, detection and diagnosis of breast cancer, and prediction of breast cancer risk, enabling more precise patient management. AI-based algorithms may also enhance the efficiency of the interpretation workflow by reducing both the workload and interpretation time. Read More

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Multi-scale attention-based convolutional neural network for classification of breast masses in mammograms.

Med Phys 2021 May 13. Epub 2021 May 13.

College of Data Science, Taiyuan University of Technology, Taiyuan, 030600, China.

Purpose: Breast cancer is the cancer with the highest incidence in women, and early detection can effectively improve the survival rate of patients. Mammography is an important method for physicians to screening breast cancer, but the diagnosis of mammograms by physicians depends largely on clinical practice experience. Studies have shown that using computer-aided diagnosis techniques can help doctors diagnose breast cancer. Read More

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Augmenting Transfer Learning with Feature Extraction Techniques for Limited Breast Imaging Datasets.

J Digit Imaging 2021 May 10. Epub 2021 May 10.

Department of Computer Science & Engineering, Anna University, Chennai-600025, Tamil Nadu, India.

Computer aided detection (CADe) and computer aided diagnostic (CADx) systems are ongoing research areas for identifying lesions among complex inner structures with different pixel intensities, and for medical image classification. There are several techniques available for breast cancer detection and diagnosis using CADe and CADx systems. However, some of these systems are not accurate enough or suffer from lack of sufficient data. Read More

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Internet of medical things embedding deep learning with data augmentation for mammogram density classification.

Microsc Res Tech 2021 Apr 27. Epub 2021 Apr 27.

Department of Computer Science & Software Engineering, International Islamic University, Islamabad, Pakistan.

Females are approximately half of the total population worldwide, and most of them are victims of breast cancer (BC). Computer-aided diagnosis (CAD) frameworks can help radiologists to find breast density (BD), which further helps in BC detection precisely. This research detects BD automatically using mammogram images based on Internet of Medical Things (IoMT) supported devices. Read More

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Convolutional neural networks for breast cancer detection in mammography: A survey.

Comput Biol Med 2021 Apr 9;131:104248. Epub 2021 Feb 9.

University of Miami, Department of Electrical and Computer Engineering, Memorial Dr, Coral Gables, FL, 33146, USA. Electronic address:

Despite its proven record as a breast cancer screening tool, mammography remains labor-intensive and has recognized limitations, including low sensitivity in women with dense breast tissue. In the last ten years, Neural Network advances have been applied to mammography to help radiologists increase their efficiency and accuracy. This survey aims to present, in an organized and structured manner, the current knowledge base of convolutional neural networks (CNNs) in mammography. Read More

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Feature-sensitive Deep Convolutional Neural Network for Multi-instance Breast Cancer Detection.

IEEE/ACM Trans Comput Biol Bioinform 2021 Feb 18;PP. Epub 2021 Feb 18.

To obtain a well-performed computer-aided detection model for detecting breast cancer, it is usually needed to design an effective and efficient algorithm and a well-labeled dataset to train it. In this paper, firstly, a multi-instance mammography clinic dataset was constructed. Each case in the dataset includes a different number of instances captured from different views, it is labeled according to the pathological report, and all the instances of one case share one label. Read More

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February 2021

A computer-aided approach for automatic detection of breast masses in digital mammogram via spectral clustering and support vector machine.

Phys Eng Sci Med 2021 Mar 12;44(1):277-290. Epub 2021 Feb 12.

Biomedical Engineering Department, Science and Research Branch, Islamic Azad University, Tehran, Iran.

Breast cancer continues to be a widespread health concern all over the world. Mammography is an important method in the early detection of breast abnormalities. In recent years, using an automatic Computer-Aided Detection (CAD) system based on image processing techniques has been a more reliable interpretation in the illustration of breast distortion. Read More

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Deep learning in breast radiology: current progress and future directions.

Eur Radiol 2021 Jul 15;31(7):4872-4885. Epub 2021 Jan 15.

Department of Radiology, Seay Biomedical Building, University of Texas Southwestern Medical Center, 2201 Inwood Road, Dallas, TX, 75390, USA.

This review provides an overview of current applications of deep learning methods within breast radiology. The diagnostic capabilities of deep learning in breast radiology continue to improve, giving rise to the prospect that these methods may be integrated not only into detection and classification of breast lesions, but also into areas such as risk estimation and prediction of tumor responses to therapy. Remaining challenges include limited availability of high-quality data with expert annotations and ground truth determinations, the need for further validation of initial results, and unresolved medicolegal considerations. Read More

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DeepCAT: Deep Computer-Aided Triage of Screening Mammography.

J Digit Imaging 2021 Feb 11;34(1):27-35. Epub 2021 Jan 11.

The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA.

Although much deep learning research has focused on mammographic detection of breast cancer, relatively little attention has been paid to mammography triage for radiologist review. The purpose of this study was to develop and test DeepCAT, a deep learning system for mammography triage based on suspicion of cancer. Specifically, we evaluate DeepCAT's ability to provide two augmentations to radiologists: (1) discarding images unlikely to have cancer from radiologist review and (2) prioritization of images likely to contain cancer. Read More

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February 2021

Imaging Surveillance of Breast Cancer Survivors with Digital Mammography versus Digital Breast Tomosynthesis.

Radiology 2021 02 22;298(2):308-316. Epub 2020 Dec 22.

From the Departments of Radiology (M.B., S.M., C.D.L.) and Medicine (A.M.M.), Massachusetts General Hospital, 55 Fruit St, WAC 240; Boston, MA 02114.

Background Among breast cancer survivors, detecting a breast cancer when it is asymptomatic (rather than symptomatic) improves survival; thus, imaging surveillance in these patients is warranted. Digital breast tomosynthesis (DBT) is used for screening, but data on DBT for surveillance in this high-risk population are limited. Purpose To determine whether DBT leads to improved screening performance metrics when compared with two-dimensional digital mammography among breast cancer survivors. Read More

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February 2021

A Review on Multi-Organs Cancer Detection using Advanced Machine Learning Techniques.

Curr Med Imaging 2020 Dec 16. Epub 2020 Dec 16.

Department of Computer Science, COMSATS University (Attock Campus) Islamabad. Pakistan.

Abnormality behavior of the tumor is risky for human survival. Thus, finding cancer at the initial stage is beneficial for the reduction of mortality rate. Although it is not easy due to various factors concern with modalities, such as complex background, poor contrast, brightness issues, ill-defined borders, and shape of the infected area. Read More

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December 2020

Convolutional neural network for automated mass segmentation in mammography.

BMC Bioinformatics 2020 Dec 9;21(Suppl 1):192. Epub 2020 Dec 9.

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

Background: Automatic segmentation and localization of lesions in mammogram (MG) images are challenging even with employing advanced methods such as deep learning (DL) methods. We developed a new model based on the architecture of the semantic segmentation U-Net model to precisely segment mass lesions in MG images. The proposed end-to-end convolutional neural network (CNN) based model extracts contextual information by combining low-level and high-level features. Read More

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December 2020

Applications of Artificial Intelligence in Breast Imaging.

Radiol Clin North Am 2021 Jan;59(1):139-148

Viz.ai, San Francisco, CA, USA.

Artificial intelligence (AI) technology shows promise in breast imaging to improve both interpretive and noninterpretive tasks. AI-based screening triage may help identify normal examinations and AI-based computer-aided detection (AI-CAD) may increase cancer detection and reduce false positives. Risk assessment, quality assurance, and other workflow tasks may also be streamlined. Read More

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January 2021

YOLO Based Breast Masses Detection and Classification in Full-Field Digital Mammograms.

Comput Methods Programs Biomed 2021 Mar 4;200:105823. Epub 2020 Nov 4.

Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt.

Background And Objective: With the recent development in deep learning since 2012, the use of Convolutional Neural Networks (CNNs) in bioinformatics, especially medical imaging, achieved tremendous success. Besides that, breast masses detection and classifications in mammograms and their pathology classification are considered a critical challenge. Till now, the evaluation process of the screening mammograms is held by human readers which is considered very monotonous, tiring, lengthy, costly, and significantly prone to errors. Read More

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A Novel Fusion-Based Texture Descriptor to Improve the Detection of Architectural Distortion in Digital Mammography.

J Digit Imaging 2021 Feb 11;34(1):36-52. Epub 2020 Nov 11.

São Carlos School of Engineering, University of São Paulo (EESC/USP), São Carlos, São Paulo, Brazil.

Architectural distortion (AD) is the earliest sign of breast cancer that can be detected on a mammogram, and it is usually associated with malignant tumors. Breast cancer is one of the major causes of death among women, and the chance of cure can increase significantly when detected early. Computer-aided detection (CAD) systems have been used in clinical practice to assist radiologists with the task of detecting breast lesions. Read More

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February 2021

Wavelet Transform and Texton based Analysis for Detection of Benign and Malignant Masses.

Annu Int Conf IEEE Eng Med Biol Soc 2020 07;2020:2178-2181

Cancer has affected the human community to a large extent due to its low survival rate towards the end stage of the disease. It is asymptomatic in many cases during the initial stage. Thus the dependency on early diagnosis and regular check up increases manifold. Read More

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Observational Study to Evaluate the Clinical Efficacy of Thermalytix for Detecting Breast Cancer in Symptomatic and Asymptomatic Women.

JCO Glob Oncol 2020 10;6:1472-1480

Central Diagnostic Research Foundation Wellness, Bangalore, India.

Purpose: To evaluate the sensitivity and specificity of Thermalytix, an artificial intelligence-based computer-aided diagnostics (CADx) engine, to detect breast malignancy by comparing the CADx output with the final diagnosis derived using standard screening modalities.

Methods: This multisite observational study included 470 symptomatic and asymptomatic women who presented for a breast health checkup in two centers. Among them, 238 women had symptoms such as breast lump, nipple discharge, or breast pain, and the rest were asymptomatic. Read More

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October 2020

Predictors of mammographic microcalcifications.

Int J Cancer 2021 Mar 25;148(5):1132-1143. Epub 2020 Sep 25.

Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden.

We examined the association between established risk factors for breast cancer and microcalcification clusters and their asymmetry. A cohort study of 53 273 Swedish women aged 30 to 80 years, with comprehensive information on breast cancer risk factors and mammograms, was conducted. Total number of microcalcification clusters and the average mammographic density area were measured using a Computer Aided Detection system and the STRATUS method, respectively. Read More

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Identification of Women at High Risk of Breast Cancer Who Need Supplemental Screening.

Radiology 2020 11 8;297(2):327-333. Epub 2020 Sep 8.

From the Department of Medical Epidemiology and Biostatistics (M.E., K.C., P.H.) and Department of Oncology-Pathology (F.S.), Karolinska Institutet, Nobelsv 12A, Stockholm 171 77, Sweden; Department of Breast Radiology, Karolinska University Hospital, Stockholm, Sweden (F.S.); Department of Diagnostic Radiology, Lund University, Skåne University Hospital Malmö, Sweden (S.Z., K.L., D.F., H.S.); Department of Thoracic Radiology, Imaging and Physiology and Department of Physiology and Pharmacology, Karolinska Hospital, Stockholm, Sweden (P.L.); Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care (N.M., D.E.) and Department of Oncology (D.E.), University of Cambridge, Cambridge, England; and Department of Oncology, Södersjukhuset, Stockholm, Sweden (P.H.).

Background Mammography screening reduces breast cancer mortality, but a proportion of breast cancers are missed and are detected at later stages or develop during between-screening intervals. Purpose To develop a risk model based on negative mammograms that identifies women likely to be diagnosed with breast cancer before or at the next screening examination. Materials and Methods This study was based on the prospective screening cohort Karolinska Mammography Project for Risk Prediction of Breast Cancer (KARMA), 2011-2017. Read More

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November 2020

Computational Radiology in Breast Cancer Screening and Diagnosis Using Artificial Intelligence.

Can Assoc Radiol J 2021 Feb 31;72(1):98-108. Epub 2020 Aug 31.

Division of Breast Imaging, 71545Sunnybrook Health Sciences Centre, Toronto, Canada.

Breast cancer screening has been shown to significantly reduce mortality in women. The increased utilization of screening examinations has led to growing demands for rapid and accurate diagnostic reporting. In modern breast imaging centers, full-field digital mammography (FFDM) has replaced traditional analog mammography, and this has opened new opportunities for developing computational frameworks to automate detection and diagnosis. Read More

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February 2021

External Evaluation of 3 Commercial Artificial Intelligence Algorithms for Independent Assessment of Screening Mammograms.

JAMA Oncol 2020 10;6(10):1581-1588

Department of Oncology-Pathology, Karolinska Institute, Stockholm, Sweden.

Importance: A computer algorithm that performs at or above the level of radiologists in mammography screening assessment could improve the effectiveness of breast cancer screening.

Objective: To perform an external evaluation of 3 commercially available artificial intelligence (AI) computer-aided detection algorithms as independent mammography readers and to assess the screening performance when combined with radiologists.

Design, Setting, And Participants: This retrospective case-control study was based on a double-reader population-based mammography screening cohort of women screened at an academic hospital in Stockholm, Sweden, from 2008 to 2015. Read More

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October 2020

A Survey on Machine Learning Algorithms for the Diagnosis of Breast Masses with Mammograms.

Curr Med Imaging 2020 ;16(6):639-652

Sethu Institute of Technology, Kariapatti 626115, Virudhunagar District, Tamil Nadu, India.

Breast cancer is leading cancer among women for the past 60 years. There are no effective mechanisms for completely preventing breast cancer. Rather it can be detected at its earlier stages so that unnecessary biopsy can be reduced. Read More

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January 2020

Range of Radiologist Performance in a Population-based Screening Cohort of 1 Million Digital Mammography Examinations.

Radiology 2020 10 28;297(1):33-39. Epub 2020 Jul 28.

From the Departments of Pathology and Oncology (M.S., F.S.), Physiology and Pharmacology (K.D., P.L.), and Medical Epidemiology and Biostatistics (M.E.), Karolinska Institute, Stockholm, Sweden; Department of Radiology (M.S.) and Breast Radiology (F.S.), Karolinska University Hospital, Dalagatan 90, 113 43 Stockholm, Sweden; and the Department of Radiology, Capio Sankt Görans Hospital, Stockholm, Sweden (K.D.).

Background There is great interest in developing artificial intelligence (AI)-based computer-aided detection (CAD) systems for use in screening mammography. Comparative performance benchmarks from true screening cohorts are needed. Purpose To determine the range of human first-reader performance measures within a population-based screening cohort of 1 million screening mammograms to gauge the performance of emerging AI CAD systems. Read More

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October 2020

Application and Analysis of Biomedical Imaging Technology in Early Diagnosis of Breast Cancer.

Methods Mol Biol 2020 ;2204:63-73

Department of Kinesiology, Jianghan University, Wuhan, China.

Breast cancer is the primary malignant tumor that endangers women's health. The incidence of breast cancer is increasing rapidly in recent years. Accurate disease evaluation before treatment is the key to the selection of treatment options. Read More

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

Radiography (Lond) 2021 02 28;27(1):229-235. 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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February 2021

Evaluation of deep learning detection and classification towards computer-aided diagnosis of breast lesions in digital X-ray mammograms.

Comput Methods Programs Biomed 2020 Nov 4;196:105584. Epub 2020 Jun 4.

Department of Biomedical Engineering, College of Electronics and Information, Kyung Hee University, 1732, Deogyeong-daero, Giheung-gu, Yongin-si, Gyeonggi-do 17104, Republic of Korea. Electronic address:

Background And Objective: Deep learning detection and classification from medical imagery are key components for computer-aided diagnosis (CAD) systems to efficiently support physicians leading to an accurate diagnosis of breast lesions.

Methods: In this study, an integrated CAD system of deep learning detection and classification is proposed aiming to improve the diagnostic performance of breast lesions. First, a deep learning YOLO detector is adopted and evaluated for breast lesion detection from entire mammograms. Read More

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November 2020

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

Semin Cancer Biol 2021 Jul 9;72:214-225. 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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