769 results match your criteria Mammography - Computer-Aided Detection


How well do practicing radiologists interpret the results of CAD technology? A quantitative characterization.

Cogn Res Princ Implic 2022 Jun 20;7(1):52. Epub 2022 Jun 20.

Department of Neuroscience and Regenerative Medicine, Medical College of Georgia, Augusta University, Augusta University, DNRM, CA-2003, 1469 Laney Walker Blvd, Augusta, GA, 30912-2697, USA.

Many studies have shown that using a computer-aided detection (CAD) system does not significantly improve diagnostic accuracy in radiology, possibly because radiologists fail to interpret the CAD results properly. We tested this possibility using screening mammography as an illustrative example. We carried out two experiments, one using 28 practicing radiologists, and a second one using 25 non-professional subjects. Read More

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YOLO-LOGO: A transformer-based YOLO segmentation model for breast mass detection and segmentation in digital mammograms.

Comput Methods Programs Biomed 2022 Jun 23;221:106903. Epub 2022 May 23.

Department of Biochemistry and Medical Genetics, University of Manitoba, Room 308-Basic Medical Sciences Building, 745 Bannatyne Avenue, Winnipeg, Manitoba R3E 0J9, Canada; Department of Computer Science, University of Manitoba, Winnipeg, Canada; CancerCare Manitoba Research Institute, CancerCare Manitoba, Winnipeg, Canada. Electronic address:

Background And Objective: Both mass detection and segmentation in digital mammograms play a crucial role in early breast cancer detection and treatment. Furthermore, clinical experience has shown that they are the upstream tasks of pathological classification of breast lesions. Recent advancements in deep learning have made the analyses faster and more accurate. Read More

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Bio-Imaging-Based Machine Learning Algorithm for Breast Cancer Detection.

Diagnostics (Basel) 2022 May 3;12(5). Epub 2022 May 3.

Department of Information Systems, College of Computer Science & Information Technology, King Faisal University, Hofuf 31982, Saudi Arabia.

Breast cancer is one of the most widespread diseases in women worldwide. It leads to the second-largest mortality rate in women, especially in European countries. It occurs when malignant lumps that are cancerous start to grow in the breast cells. Read More

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A review of artificial intelligence in mammography.

Clin Imaging 2022 Aug 15;88:36-44. Epub 2022 May 15.

Associate Professor of Radiology, Director of Research and Education, Breast Imaging Section, Columbia University Medical Center, 622 West 168th Street, PB-1-301, New York, NY 10032, United States of America. Electronic address:

Breast cancer is the most common cancer among women worldwide. Mammography is the most widely used modality to detect breast cancer. Over the past decade, computer aided detection (CAD) powered by artificial intelligence (AI)/deep learning has shown significant increase in accuracy compared to the traditional CAD. Read More

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Dual-energy three-compartment breast imaging for compositional biomarkers to improve detection of malignant lesions.

Commun Med (Lond) 2021 31;1:29. Epub 2021 Aug 31.

Department of Epidemiology and Population Sciences, University of Hawaii Cancer Center, Honolulu, HI USA.

Background: While breast imaging such as full-field digital mammography and digital breast tomosynthesis have helped to reduced breast cancer mortality, issues with low specificity exist resulting in unnecessary biopsies. The fundamental information used in diagnostic decisions are primarily based in lesion morphology. We explore a dual-energy compositional breast imaging technique known as three-compartment breast (3CB) to show how the addition of compositional information improves malignancy detection. Read More

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Early detection and classification of abnormality in prior mammograms using image-to-image translation and YOLO techniques.

Comput Methods Programs Biomed 2022 Jun 13;221:106884. Epub 2022 May 13.

Department of Computer Science and Engineering, University of Louisville, Louisville, KY, 40292, USA.

Background And Objective: Computer-aided-detection (CAD) systems have been developed to assist radiologists on finding suspicious lesions in mammogram. Deep Learning technology have recently succeeded to increase the chance of recognizing abnormality at an early stage in order to avoid unnecessary biopsies and decrease the mortality rate. In this study, we investigated the effectiveness of an end-to-end fusion model based on You-Only-Look-Once (YOLO) architecture, to simultaneously detect and classify suspicious breast lesions on digital mammograms. Read More

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Spatial Distribution Analysis of Novel Texture Feature Descriptors for Accurate Breast Density Classification.

Sensors (Basel) 2022 Mar 30;22(7). Epub 2022 Mar 30.

Canterbury Breastcare, St. George's Medical Centre, Christchurch 8014, New Zealand.

Breast density has been recognised as an important biomarker that indicates the risk of developing breast cancer. Accurate classification of breast density plays a crucial role in developing a computer-aided detection (CADe) system for mammogram interpretation. This paper proposes a novel texture descriptor, namely, rotation invariant uniform local quinary patterns (RIU4-LQP), to describe texture patterns in mammograms and to improve the robustness of image features. Read More

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Architectural distortion detection based on superior-inferior directional context and anatomic prior knowledge in digital breast tomosynthesis.

Med Phys 2022 Jun 5;49(6):3749-3768. Epub 2022 Apr 5.

School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China.

Background: In 2020, breast cancer becomes the most leading diagnosed cancer all over the world. The burden is increasing in the prevention and treatment of breast cancer. Accurately detecting breast lesions in screening images is important for early detection of cancer. Read More

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Prediction of Short-Term Breast Cancer Risk with Fusion of CC- and MLO-Based Risk Models in Four-View Mammograms.

J Digit Imaging 2022 Mar 9. Epub 2022 Mar 9.

Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, 310018, People's Republic of China.

This study performed and assessed a novel program to improve the accuracy of short-term breast cancer risk prediction by using information from craniocaudal (CC) and mediolateral-oblique (MLO) views of two breasts. An age-matched dataset of 556 patients with at least two sequential full-field digital mammography examinations was applied. In the second examination, 278 cases were diagnosed and pathologically verified as cancer, and 278 were negative, while all cases in the first examination were negative (not recalled). Read More

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Retrospective Review of Missed Cancer Detection and Its Mammography Findings with Artificial-Intelligence-Based, Computer-Aided Diagnosis.

Diagnostics (Basel) 2022 Feb 2;12(2). Epub 2022 Feb 2.

Department of Radiology, College of Medicine, Seoul Saint Mary's Hospital, The Catholic University of Korea, Seoul 06591, Korea.

To investigate whether artificial-intelligence-based, computer-aided diagnosis (AI-CAD) could facilitate the detection of missed cancer on digital mammography, a total of 204 women diagnosed with breast cancer with diagnostic (present) and prior mammograms between 2018 and 2020 were included in this study. Two breast radiologists reviewed the mammographic features and classified them into true negative, minimal sign or missed cancer. They analyzed the AI-CAD results with an abnormality score and assessed whether the AI-CAD correctly localized the known cancer sites. Read More

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February 2022

Improved automated early detection of breast cancer based on high resolution 3D micro-CT microcalcification images.

BMC Cancer 2022 Feb 11;22(1):162. Epub 2022 Feb 11.

Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel (VUB), Pleinlaan 2, Brussels, B-1050, Belgium.

Background: The detection of suspicious microcalcifications on mammography represents one of the earliest signs of a malignant breast tumor. Assessing microcalcifications' characteristics based on their appearance on 2D breast imaging modalities is in many cases challenging for radiologists. The aims of this study were to: (a) analyse the association of shape and texture properties of breast microcalcifications (extracted by scanning breast tissue with a high resolution 3D scanner) with malignancy, (b) evaluate microcalcifications' potential to diagnose benign/malignant patients. Read More

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February 2022

Artificial Intelligence for Breast Cancer Screening in Mammography (AI-STREAM): A Prospective Multicenter Study Design in Korea Using AI-Based CADe/x.

J Breast Cancer 2022 Feb 6;25(1):57-68. Epub 2022 Jan 6.

Department of Radiology, Kyung Hee University Hospital at Gangdong, College of Medicine, Kyung Hee University, Seoul, Korea.

Purpose: Artificial intelligence (AI)-based computer-aided detection/diagnosis (CADe/x) has helped improve radiologists' performance and provides results equivalent or superior to those of radiologists' alone. This prospective multicenter cohort study aims to generate real-world evidence on the overall benefits and disadvantages of using AI-based CADe/x for breast cancer detection in a population-based breast cancer screening program comprising Korean women aged ≥ 40 years. The purpose of this report is to compare the diagnostic accuracy of radiologists with and without the use of AI-based CADe/x in mammography readings for breast cancer screening of Korean women with average breast cancer risk. Read More

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February 2022

Deep learning model improves radiologists' performance in detection and classification of breast lesions.

Chin J Cancer Res 2021 Dec;33(6):682-693

Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiology, Peking University Cancer Hospital & Institute, Beijing 100142, China.

Objective: Computer-aided diagnosis using deep learning algorithms has been initially applied in the field of mammography, but there is no large-scale clinical application.

Methods: This study proposed to develop and verify an artificial intelligence model based on mammography. Firstly, mammograms retrospectively collected from six centers were randomized to a training dataset and a validation dataset for establishing the model. Read More

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December 2021

Deep learning-based breast region extraction of mammographic images combining pre-processing methods and semantic segmentation supported by Deeplab v3.

Technol Health Care 2022 ;30(S1):173-190

Background: Breast cancer has long been one of the major global life-threatening illnesses among women. Surgery and adjuvant therapy, coupled with early detection, could save many lives. This underscores the importance of mammography, a cost-effective and accurate method for early detection. Read More

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The optimal use of computer aided detection to find low prevalence cancers.

Authors:
Melina A Kunar

Cogn Res Princ Implic 2022 02 4;7(1):13. Epub 2022 Feb 4.

Department of Psychology, The University of Warwick, Coventry, CV4 7AL, UK.

People miss a high proportion of targets that only appear rarely. This low prevalence (LP) effect has implications for applied search tasks such as the clinical reading of mammograms. Computer aided detection (CAD) has been used to help radiologists search mammograms by highlighting areas likely to contain a cancer. Read More

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February 2022

Quantifying lesion enhancement on contrast-enhanced mammography: a review of published data.

Clin Radiol 2022 04 26;77(4):e313-e320. Epub 2022 Jan 26.

Nottingham University Hospitals NHS Trust, Nottingham, UK.

Aim: To review and discuss the current published data on FUNCTIONAL DATA DERIVED FROM contrast-enhanced spectral mammography (CESM) for investigation of breast lesions.

Materials And Methods: Literature searches were conducted in MEDLINE and PUBMED. Due to the novel nature of CESM and sparsity of published literature pertaining to associated functional data, the Medical Subject Headings (MeSH) used were intentionally broad. Read More

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PeMNet for Pectoral Muscle Segmentation.

Biology (Basel) 2022 Jan 14;11(1). Epub 2022 Jan 14.

School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK.

As an important imaging modality, mammography is considered to be the global gold standard for early detection of breast cancer. Computer-Aided (CAD) systems have played a crucial role in facilitating quicker diagnostic procedures, which otherwise could take weeks if only radiologists were involved. In some of these CAD systems, breast pectoral segmentation is required for breast region partition from breast pectoral muscle for specific analysis tasks. Read More

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January 2022

Architectural Distortion-Based Digital Mammograms Classification Using Depth Wise Convolutional Neural Network.

Biology (Basel) 2021 Dec 23;11(1). Epub 2021 Dec 23.

The School of Software Engineering, Beijing University of Technology, Beijing 100024, China.

Architectural distortion is the third most suspicious appearance on a mammogram representing abnormal regions. Architectural distortion (AD) detection from mammograms is challenging due to its subtle and varying asymmetry on breast mass and small size. Automatic detection of abnormal ADs regions in mammograms using computer algorithms at initial stages could help radiologists and doctors. Read More

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December 2021

Discriminative Localized Sparse Approximations for Mass Characterization in Mammograms.

Front Oncol 2021 30;11:725320. Epub 2021 Dec 30.

Math Imaging and Visual Computing Lab, Division of Physics, Engineering, Mathematics and Computer Science, Delaware State University, Dover, DE, United States.

The most common form of cancer among women in both developed and developing countries is breast cancer. The early detection and diagnosis of this disease is significant because it may reduce the number of deaths caused by breast cancer and improve the quality of life of those effected. Computer-aided detection (CADe) and computer-aided diagnosis (CADx) methods have shown promise in recent years for aiding in the human expert reading analysis and improving the accuracy and reproducibility of pathology results. Read More

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December 2021

Artificial Intelligence Detection of Missed Cancers at Digital Mammography That Were Detected at Digital Breast Tomosynthesis.

Radiol Artif Intell 2021 Nov 1;3(6):e200299. Epub 2021 Sep 1.

Diagnostic Radiology (V.D., I.A., K.L., S.Z., M.D.) and Medical Radiation Physics (A.T., M.D.), Department of Translational Medicine, Lund University, Malmö, Sweden; and Department of Medical Imaging and Physiology (V.D., S.Z.), Unilabs Breast Centre (I.A., K.L.), and Department of Radiation Physics (A.T.), Skåne University Hospital, Carl Bertil Laurells gata 9, 205 02 Malmö, Sweden.

Purpose: To investigate how an artificial intelligence (AI) system performs at digital mammography (DM) from a screening population with ground truth defined by digital breast tomosynthesis (DBT), and whether AI could detect breast cancers at DM that had originally only been detected at DBT.

Materials And Methods: In this secondary analysis of data from a prospective study, DM examinations from 14 768 women (mean age, 57 years), examined with both DM and DBT with independent double reading in the Malmӧ Breast Tomosynthesis Screening Trial (MBTST) (ClinicalTrials.gov: NCT01091545; data collection, 2010-2015), were analyzed with an AI system. Read More

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November 2021

Breast histopathological image analysis using image processing techniques for diagnostic puposes: A methodological review.

J Med Syst 2021 Dec 3;46(1). Epub 2021 Dec 3.

Kasturbha Medical College, Manipal Academy of Higher Education, Manipal, India.

Breast cancer in women is the second most common cancer worldwide. Early detection of breast cancer can reduce the risk of human life. Non-invasive techniques such as mammograms and ultrasound imaging are popularly used to detect the tumour. Read More

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December 2021

Anomaly Detection of Calcifications in Mammography Based on 11,000 Negative Cases.

IEEE Trans Biomed Eng 2022 05 21;69(5):1639-1650. Epub 2022 Apr 21.

In mammography, calcifications are one of the most common signs of breast cancer. Detection of such lesions is an active area of research for computer-aided diagnosis and machine learning algorithms. Due to limited numbers of positive cases, many supervised detection models suffer from overfitting and fail to generalize. Read More

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Clinical Artificial Intelligence Applications: Breast Imaging.

Radiol Clin North Am 2021 Nov;59(6):1027-1043

Committee on Medical Physics, Department of Radiology, The University of Chicago, 5841 S Maryland Avenue, MC2026, Chicago, IL 60637, USA. Electronic address:

This article gives a brief overview of the development of artificial intelligence in clinical breast imaging. For multiple decades, artificial intelligence (AI) methods have been developed and translated for breast imaging tasks such as detection, diagnosis, and assessing response to therapy. As imaging modalities arise to support breast cancer screening programs and diagnostic examinations, including full-field digital mammography, breast tomosynthesis, ultrasound, and MRI, AI techniques parallel the efforts with more complex algorithms, faster computers, and larger data sets. Read More

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November 2021

A Bottom-Up Review of Image Analysis Methods for Suspicious Region Detection in Mammograms.

J Imaging 2021 Sep 18;7(9). Epub 2021 Sep 18.

Department of Computing and Informatics, Bournemouth University, Poole, Dorset BH12 5BB, UK.

Breast cancer is one of the most common death causes amongst women all over the world. Early detection of breast cancer plays a critical role in increasing the survival rate. Various imaging modalities, such as mammography, breast MRI, ultrasound and thermography, are used to detect breast cancer. Read More

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September 2021

Study on Data Partition for Delimitation of Masses in Mammography.

J Imaging 2021 Sep 2;7(9). Epub 2021 Sep 2.

Polytechnic of Coimbra-ISEC, Rua Pedro Nunes, Quinta da Nora, 3030-199 Coimbra, Portugal.

Mammography is the primary medical imaging method used for routine screening and early detection of breast cancer in women. However, the process of manually inspecting, detecting, and delimiting the tumoral massess in 2D images is a very time-consuming task, subject to human errors due to fatigue. Therefore, integrated computer-aided detection systems have been proposed, based on modern computer vision and machine learning methods. Read More

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September 2021

Improved Inception V3 method and its effect on radiologists' performance of tumor classification with automated breast ultrasound system.

Gland Surg 2021 Jul;10(7):2232-2245

Department of Ultrasound, Taizhou Hospital of Zhejiang Province, Zhejiang University, Linhai, China.

Background: The automated breast ultrasound system (ABUS) is recognized as a valuable detection tool in addition to mammography. The purpose of this study was to propose a novel computer-aided diagnosis (CAD) system by extracting the textural features from ABUS images and to investigate the efficiency of using this CAD for breast cancer detection.

Methods: This retrospective study involved 149 breast nodules [maximum diameter: mean size 18. Read More

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Mammographic Surveillance After Breast-Conserving Therapy: Impact of Digital Breast Tomosynthesis and Artificial Intelligence-Based Computer-Aided Detection.

AJR Am J Roentgenol 2022 01 11;218(1):42-51. Epub 2021 Aug 11.

Department of Radiology, Wonju Severance Christian Hospital, Yonsei University Wonju College of Medicine, 20 Ilsan-ro, Wonju 220-701, Korea.

Postoperative mammograms present interpretive challenges due to postoperative distortion and hematomas. The application of digital breast tomosyn-thesis (DBT) and artificial intelligence-based computer-aided detection (AI-CAD) after breast-conserving therapy (BCT) has not been widely investigated. The purpose of our study was to assess the impact of additional DBT or AI-CAD on recall rate and diagnostic performance in women undergoing mammographic surveillance after BCT. Read More

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January 2022

Thermography as an Economical Alternative Modality to Mammography for Early Detection of Breast Cancer.

J Healthc Eng 2021 31;2021:5543101. Epub 2021 Jul 31.

Department of Electrical and Instrumentation, Sant Longowal Institute of Engineering and Technology, Longowal 148106, India.

Breast cancer has become a menacing form of cancer among women accounting for 11.6% of total deaths of 9.6 million due to all types of cancer every year all over the world. Read More

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December 2021

Dual Convolutional Neural Networks for Breast Mass Segmentation and Diagnosis in Mammography.

IEEE Trans Med Imaging 2022 01 30;41(1):3-13. Epub 2021 Dec 30.

Deep convolutional neural networks (CNNs) have emerged as a new paradigm for Mammogram diagnosis. Contemporary CNN-based computer-aided-diagnosis systems (CADs) for breast cancer directly extract latent features from input mammogram image and ignore the importance of morphological features. In this paper, we introduce a novel end-to-end deep learning framework for mammogram image processing, which computes mass segmentation and simultaneously predicts diagnosis results. Read More

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January 2022