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Loizidou K, Elia R, Pitris C. Computer-aided breast cancer detection and classification in mammography: A comprehensive review. Comput Biol Med 2023; 153:106554. [PMID: 36646021 DOI: 10.1016/j.compbiomed.2023.106554] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 12/13/2022] [Accepted: 01/11/2023] [Indexed: 01/15/2023]
Abstract
Cancer is the second cause of mortality worldwide and it has been identified as a perilous disease. Breast cancer accounts for ∼20% of all new cancer cases worldwide, making it a major cause of morbidity and mortality. Mammography is an effective screening tool for the early detection and management of breast cancer. However, the identification and interpretation of breast lesions is challenging even for expert radiologists. For that reason, several Computer-Aided Diagnosis (CAD) systems are being developed to assist radiologists to accurately detect and/or classify breast cancer. This review examines the recent literature on the automatic detection and/or classification of breast cancer in mammograms, using both conventional feature-based machine learning and deep learning algorithms. The review begins with a comparison of algorithms developed specifically for the detection and/or classification of two types of breast abnormalities, micro-calcifications and masses, followed by the use of sequential mammograms for improving the performance of the algorithms. The available Food and Drug Administration (FDA) approved CAD systems related to triage and diagnosis of breast cancer in mammograms are subsequently presented. Finally, a description of the open access mammography datasets is provided and the potential opportunities for future work in this field are highlighted. The comprehensive review provided here can serve both as a thorough introduction to the field but also provide indicative directions to guide future applications.
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Affiliation(s)
- Kosmia Loizidou
- KIOS Research and Innovation Center of Excellence, Department of Electrical and Computer Engineering, University of Cyprus, Nicosia, Cyprus.
| | - Rafaella Elia
- KIOS Research and Innovation Center of Excellence, Department of Electrical and Computer Engineering, University of Cyprus, Nicosia, Cyprus.
| | - Costas Pitris
- KIOS Research and Innovation Center of Excellence, Department of Electrical and Computer Engineering, University of Cyprus, Nicosia, Cyprus.
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2
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din NMU, Dar RA, Rasool M, Assad A. Breast cancer detection using deep learning: Datasets, methods, and challenges ahead. Comput Biol Med 2022; 149:106073. [DOI: 10.1016/j.compbiomed.2022.106073] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Revised: 08/21/2022] [Accepted: 08/27/2022] [Indexed: 12/22/2022]
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3
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Oza P, Sharma P, Patel S, Bruno A. A Bottom-Up Review of Image Analysis Methods for Suspicious Region Detection in Mammograms. J Imaging 2021; 7:190. [PMID: 34564116 PMCID: PMC8466003 DOI: 10.3390/jimaging7090190] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Revised: 09/09/2021] [Accepted: 09/14/2021] [Indexed: 11/17/2022] Open
Abstract
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. Though there is a considerable success with mammography in biomedical imaging, detecting suspicious areas remains a challenge because, due to the manual examination and variations in shape, size, other mass morphological features, mammography accuracy changes with the density of the breast. Furthermore, going through the analysis of many mammograms per day can be a tedious task for radiologists and practitioners. One of the main objectives of biomedical imaging is to provide radiologists and practitioners with tools to help them identify all suspicious regions in a given image. Computer-aided mass detection in mammograms can serve as a second opinion tool to help radiologists avoid running into oversight errors. The scientific community has made much progress in this topic, and several approaches have been proposed along the way. Following a bottom-up narrative, this paper surveys different scientific methodologies and techniques to detect suspicious regions in mammograms spanning from methods based on low-level image features to the most recent novelties in AI-based approaches. Both theoretical and practical grounds are provided across the paper sections to highlight the pros and cons of different methodologies. The paper's main scope is to let readers embark on a journey through a fully comprehensive description of techniques, strategies and datasets on the topic.
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Affiliation(s)
- Parita Oza
- Computer Science and Engineering Department, School of Technology, Pandit Deendayal Energy University, Gandhinagar 382007, India; (P.S.); (S.P.)
| | - Paawan Sharma
- Computer Science and Engineering Department, School of Technology, Pandit Deendayal Energy University, Gandhinagar 382007, India; (P.S.); (S.P.)
| | - Samir Patel
- Computer Science and Engineering Department, School of Technology, Pandit Deendayal Energy University, Gandhinagar 382007, India; (P.S.); (S.P.)
| | - Alessandro Bruno
- Department of Computing and Informatics, Bournemouth University, Poole, Dorset BH12 5BB, UK
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Zeiser FA, da Costa CA, Zonta T, Marques NMC, Roehe AV, Moreno M, da Rosa Righi R. Segmentation of Masses on Mammograms Using Data Augmentation and Deep Learning. J Digit Imaging 2021; 33:858-868. [PMID: 32206943 DOI: 10.1007/s10278-020-00330-4] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Abstract
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. In this article, we propose a computer-aided detection (CAD) system to aid in the diagnosis of masses in digitized mammograms using a model based in the U-Net, allowing specialists to monitor the lesion over time. Unlike most of the studies, we propose the use of an entire base of digitized mammograms using normal, benign, and malignant cases. Our research is divided into four stages: (1) pre-processing, with the removal of irrelevant information, enhancement of the contrast of 7989 images of the Digital Database for Screening Mammography (DDSM), and obtaining regions of interest. (2) Data augmentation, with horizontal mirroring, zooming, and resizing of images; (3) training, with tests of six-based U-Net models, with different characteristics; (4) testing, evaluating four metrics, accuracy, sensitivity, specificity, and Dice Index. The tested models obtained different results regarding the assessed parameters. The best model achieved a sensitivity of 92.32%, specificity of 80.47%, accuracy of 85.95% Dice Index of 79.39%, and AUC of 86.40%. Even using a full base without case selection bias, the results obtained demonstrate that the use of a complete database can provide knowledge to the CAD expert.
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Affiliation(s)
- Felipe André Zeiser
- Software Innovation Laboratory - SOFTWARELAB, Applied Computing Graduate Program, Universidade do Vale do Rio dos Sinos - Unisinos, Av. Unisinos 950, São Leopoldo, 93022-000, Brazil
| | - Cristiano André da Costa
- Software Innovation Laboratory - SOFTWARELAB, Applied Computing Graduate Program, Universidade do Vale do Rio dos Sinos - Unisinos, Av. Unisinos 950, São Leopoldo, 93022-000, Brazil.
| | - Tiago Zonta
- Software Innovation Laboratory - SOFTWARELAB, Applied Computing Graduate Program, Universidade do Vale do Rio dos Sinos - Unisinos, Av. Unisinos 950, São Leopoldo, 93022-000, Brazil.,Ciências da Vida em Pesquisa, Universidade do Oeste de Santa Catarina, Chapecó, Brazil
| | - Nuno M C Marques
- Departamento de Informática, Universidade Nova de Lisboa, Almada, Portugal
| | - Adriana Vial Roehe
- Departamento de Patologia e Medicina Legal, Universidade Federal de Ciências da Saúde de, Porto Alegre, Brazil
| | - Marcelo Moreno
- Estudos Biológicos e Clínicos em Patologias Humanas, Universidade Federal da Fronteira Sul, Chapecó, Brazil
| | - Rodrigo da Rosa Righi
- Software Innovation Laboratory - SOFTWARELAB, Applied Computing Graduate Program, Universidade do Vale do Rio dos Sinos - Unisinos, Av. Unisinos 950, São Leopoldo, 93022-000, Brazil
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Yang Z, Cao Z, Zhang Y, Tang Y, Lin X, Ouyang R, Wu M, Han M, Xiao J, Huang L, Wu S, Chang P, Ma J. MommiNet-v2: Mammographic multi-view mass identification networks. Med Image Anal 2021; 73:102204. [PMID: 34399154 DOI: 10.1016/j.media.2021.102204] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2020] [Revised: 04/12/2021] [Accepted: 06/08/2021] [Indexed: 11/28/2022]
Abstract
Many existing approaches for mammogram analysis are based on single view. Some recent DNN-based multi-view approaches can perform either bilateral or ipsilateral analysis, while in practice, radiologists use both to achieve the best clinical outcome. MommiNet is the first DNN-based tri-view mass identification approach, which can simultaneously perform bilateral and ipsilateral analysis of mammographic images, and in turn, can fully emulate the radiologists' reading practice. In this paper, we present MommiNet-v2, with improved network architecture and performance. Novel high-resolution network (HRNet)-based architectures are proposed to learn the symmetry and geometry constraints, to fully aggregate the information from all views for accurate mass detection. A multi-task learning scheme is adopted to incorporate both Breast Imaging-Reporting and Data System (BI-RADS) and biopsy information to train a mass malignancy classification network. Extensive experiments have been conducted on the public DDSM (Digital Database for Screening Mammography) dataset and our in-house dataset, and state-of-the-art results have been achieved in terms of mass detection accuracy. Satisfactory mass malignancy classification result has also been obtained on our in-house dataset.
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Affiliation(s)
| | | | | | | | - Xiaohui Lin
- Department of Radiology, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University), Shenzhen, Guangdong 518020, China
| | - Rushan Ouyang
- Department of Radiology, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University), Shenzhen, Guangdong 518020, China
| | - Mingxiang Wu
- Department of Radiology, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University), Shenzhen, Guangdong 518020, China
| | - Mei Han
- PAII Inc., Palo Alto, CA 94306, USA
| | - Jing Xiao
- Ping An Technology Co., Ltd., Shenzhen 518000, China
| | - Lingyun Huang
- Ping An Technology Co., Ltd., Shenzhen 518000, China
| | - Shibin Wu
- Ping An Technology Co., Ltd., Shenzhen 518000, China
| | | | - Jie Ma
- Department of Radiology, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University), Shenzhen, Guangdong 518020, China
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Cao H, Pu S, Tan W, Tong J. Breast mass detection in digital mammography based on anchor-free architecture. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 205:106033. [PMID: 33845408 DOI: 10.1016/j.cmpb.2021.106033] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Accepted: 02/27/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND AND OBJECTIVE Accurate detection of breast masses in mammography images is critical to diagnose early breast cancer, which can greatly improve the patients' survival rate. However, it is still a big challenge due to the heterogeneity of breast masses and the complexity of their surrounding environment. Therefore, how to develop a robust breast mass detection framework in clinical practical applications to improve patient survival is a topic that researchers need to continue to explore. METHODS To address these problems, we propose a one-stage object detection architecture, called Breast Mass Detection Network (BMassDNet), based on anchor-free and feature pyramid which makes the detection of breast masses of different sizes well adapted. We introduce a truncation normalization method and combine it with adaptive histogram equalization to enhance the contrast between the breast mass and the surrounding environment. Meanwhile, to solve the overfitting problem caused by small data size, we propose a natural deformation data augmentation method and mend the train data dynamic updating method based on the data complexity to effectively utilize the limited data. Finally, we use transfer learning to assist the training process and to improve the robustness of the model ulteriorly. RESULTS On the INbreast dataset, each image has an average of 0.495 false positives whilst the recall rate is 0.930; On the DDSM dataset, when each image has 0.599 false positives, the recall rate reaches 0.943. CONCLUSIONS The experimental results on datasets INbreast and DDSM show that the proposed BMassDNet can obtain competitive detection performance over the current top ranked methods.
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Affiliation(s)
- Haichao Cao
- Hikvision Digital Technology Company Limited, Hangzhou310051, China
| | - Shiliang Pu
- Hikvision Digital Technology Company Limited, Hangzhou310051, China.
| | - Wenming Tan
- Hikvision Digital Technology Company Limited, Hangzhou310051, China
| | - Junyan Tong
- Hikvision Digital Technology Company Limited, Hangzhou310051, China
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7
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Automatic three-dimensional reconstruction of subsurface defects by segmenting ultrasonic point cloud. J Taiwan Inst Chem Eng 2021. [DOI: 10.1016/j.jtice.2021.03.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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8
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A review of breast boundary and pectoral muscle segmentation methods in computer-aided detection/diagnosis of breast mammography. Artif Intell Rev 2020. [DOI: 10.1007/s10462-019-09721-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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9
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de Sampaio WB, de Oliveira FSS, de Carvalho Filho AO, Silva AC, de Paiva AC, Gattass M. Classification of breast tissues into mass and non-mass by means of the micro-genetic algorithm, phylogenetic trees, LBP and SVM. COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING: IMAGING & VISUALIZATION 2018. [DOI: 10.1080/21681163.2016.1240630] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Wener Borges de Sampaio
- Applied Computing Group – NCA, Federal University of Maranhão UFMA, Campus do Bacanga, São Lus, Brazil
| | | | | | - Aristófanes Corrêa Silva
- Applied Computing Group – NCA, Federal University of Maranhão UFMA, Campus do Bacanga, São Lus, Brazil
| | - Anselmo Cardoso de Paiva
- Applied Computing Group – NCA, Federal University of Maranhão UFMA, Campus do Bacanga, São Lus, Brazil
| | - Marcelo Gattass
- Computer Science Department, Pontifical Catholic University of Rio de Janeiro – PUC-Rio, Rio de Janeiro, Brazil
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10
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Yassin NIR, Omran S, El Houby EMF, Allam H. Machine learning techniques for breast cancer computer aided diagnosis using different image modalities: A systematic review. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 156:25-45. [PMID: 29428074 DOI: 10.1016/j.cmpb.2017.12.012] [Citation(s) in RCA: 120] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2017] [Revised: 11/26/2017] [Accepted: 12/11/2017] [Indexed: 06/08/2023]
Abstract
BACKGROUND AND OBJECTIVE The high incidence of breast cancer in women has increased significantly in the recent years. Physician experience of diagnosing and detecting breast cancer can be assisted by using some computerized features extraction and classification algorithms. This paper presents the conduction and results of a systematic review (SR) that aims to investigate the state of the art regarding the computer aided diagnosis/detection (CAD) systems for breast cancer. METHODS The SR was conducted using a comprehensive selection of scientific databases as reference sources, allowing access to diverse publications in the field. The scientific databases used are Springer Link (SL), Science Direct (SD), IEEE Xplore Digital Library, and PubMed. Inclusion and exclusion criteria were defined and applied to each retrieved work to select those of interest. From 320 studies retrieved, 154 studies were included. However, the scope of this research is limited to scientific and academic works and excludes commercial interests. RESULTS This survey provides a general analysis of the current status of CAD systems according to the used image modalities and the machine learning based classifiers. Potential research studies have been discussed to create a more objective and efficient CAD systems.
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Affiliation(s)
- Nisreen I R Yassin
- Systems & Information Department, Engineering Research Division, National Research Centre, Dokki, Cairo 12311, Egypt.
| | - Shaimaa Omran
- Systems & Information Department, Engineering Research Division, National Research Centre, Dokki, Cairo 12311, Egypt.
| | - Enas M F El Houby
- Systems & Information Department, Engineering Research Division, National Research Centre, Dokki, Cairo 12311, Egypt.
| | - Hemat Allam
- Anaesthesia & Pain, Medical Division, National Research Centre, Dokki, Cairo 12311, Egypt.
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Isikli Esener I, Ergin S, Yuksel T. A New Feature Ensemble with a Multistage Classification Scheme for Breast Cancer Diagnosis. JOURNAL OF HEALTHCARE ENGINEERING 2017; 2017:3895164. [PMID: 29065592 PMCID: PMC5494793 DOI: 10.1155/2017/3895164] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/07/2017] [Revised: 03/11/2017] [Accepted: 04/06/2017] [Indexed: 11/21/2022]
Abstract
A new and effective feature ensemble with a multistage classification is proposed to be implemented in a computer-aided diagnosis (CAD) system for breast cancer diagnosis. A publicly available mammogram image dataset collected during the Image Retrieval in Medical Applications (IRMA) project is utilized to verify the suggested feature ensemble and multistage classification. In achieving the CAD system, feature extraction is performed on the mammogram region of interest (ROI) images which are preprocessed by applying a histogram equalization followed by a nonlocal means filtering. The proposed feature ensemble is formed by concatenating the local configuration pattern-based, statistical, and frequency domain features. The classification process of these features is implemented in three cases: a one-stage study, a two-stage study, and a three-stage study. Eight well-known classifiers are used in all cases of this multistage classification scheme. Additionally, the results of the classifiers that provide the top three performances are combined via a majority voting technique to improve the recognition accuracy on both two- and three-stage studies. A maximum of 85.47%, 88.79%, and 93.52% classification accuracies are attained by the one-, two-, and three-stage studies, respectively. The proposed multistage classification scheme is more effective than the single-stage classification for breast cancer diagnosis.
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Affiliation(s)
- Idil Isikli Esener
- Department of Electrical Electronics Engineering, Bilecik Seyh Edebali University, 11210 Bilecik, Turkey
| | - Semih Ergin
- Department of Electrical Electronics Engineering, Eskisehir Osmangazi University, 26480 Eskisehir, Turkey
| | - Tolga Yuksel
- Department of Electrical Electronics Engineering, Bilecik Seyh Edebali University, 11210 Bilecik, Turkey
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