Application of deep learning in the detection of breast lesions with four different breast densities

Li, Hongmei; Ye, Jing; Líu, Hao; Wang, Yichuan; Shi, Binbin; Chen, Juan; Ai-ping, Kong; Xu, Qing; Cai, Junhui

doi:10.1002/cam4.4042

Cited by 9 publications

(4 citation statements)

References 26 publications

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“…One hundred and seven articles were included in the final analysis. 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 …”

Section: Resultsmentioning

confidence: 99%

Reproducibility and Explainability of Deep Learning in Mammography: A Systematic Review of Literature

Bhalla,

Rangarajan,

Chandra

et al. 2023

Indian J Radiol Imaging

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Background Although abundant literature is currently available on the use of deep learning for breast cancer detection in mammography, the quality of such literature is widely variable. Purpose To evaluate published literature on breast cancer detection in mammography for reproducibility and to ascertain best practices for model design. Methods The PubMed and Scopus databases were searched to identify records that described the use of deep learning to detect lesions or classify images into cancer or noncancer. A modification of Quality Assessment of Diagnostic Accuracy Studies (mQUADAS-2) tool was developed for this review and was applied to the included studies. Results of reported studies (area under curve [AUC] of receiver operator curve [ROC] curve, sensitivity, specificity) were recorded. Results A total of 12,123 records were screened, of which 107 fit the inclusion criteria. Training and test datasets, key idea behind model architecture, and results were recorded for these studies. Based on mQUADAS-2 assessment, 103 studies had high risk of bias due to nonrepresentative patient selection. Four studies were of adequate quality, of which three trained their own model, and one used a commercial network. Ensemble models were used in two of these. Common strategies used for model training included patch classifiers, image classification networks (ResNet in 67%), and object detection networks (RetinaNet in 67%). The highest reported AUC was 0.927 ± 0.008 on a screening dataset, while it reached 0.945 (0.919–0.968) on an enriched subset. Higher values of AUC (0.955) and specificity (98.5%) were reached when combined radiologist and Artificial Intelligence readings were used than either of them alone. None of the studies provided explainability beyond localization accuracy. None of the studies have studied interaction between AI and radiologist in a real world setting. Conclusion While deep learning holds much promise in mammography interpretation, evaluation in a reproducible clinical setting and explainable networks are the need of the hour.

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Section: Resultsmentioning

confidence: 99%

Reproducibility and Explainability of Deep Learning in Mammography: A Systematic Review of Literature

Bhalla,

Rangarajan,

Chandra

et al. 2023

Indian J Radiol Imaging

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“…This technique improves the information about breast mass. (Ull et al, 2023;Bandaru et al, 2022a;Duong et al, 2023;Kahnouei et al, 2022;P and V, 2022;Li et al, 2019aLi et al, , 2021cMahmood et al, 2022;Wang et al, 2022;Yao et al, 2022), (Tsochatzidis et al, 2021;Ahmed et al, 2020;Fathy and Ghoneim, 2019;Frazer et al, 2021;Sannasi Chakravarthy and Rajaguru, 2021;Chakravarthy and Rajaguru, 2022;Al-Antari et al, 2020;Al-Mansour et al, 2022;Alruwaili and Gouda, 2022;Altameem et al, 2022;Altaf, 2021;Cao et al, 2020;Al-Tam et al, 2022;Bandaru et al, 2022b;López-Cabrera et al, 2020;Saber et al, 2021;Chougrad et al, 2018;Falconi et al, 2020;Jafarzadeh Ghoushchi et al, 2021;Hanis et al, 2023;Lin et al, 2022;Mokni and Haoues, 2022;Mudeng et al, 2022;Khan and Masala, 2023;Oza et al, 2023;Prodan et al, 2023;Ragab et al, 2021;Yu et al, 2023b;Gerbasi et al, 2023;Shanker and Vadivel, 2022;Zhang and Wang, 2019;Adedigba et al, 2022;…”

Section: Techniques References Descriptionmentioning

confidence: 99%

A Systematic Literature Review on Mammography: Deep Learning in Redefining Breast Cancer Diagnosis for the Asian Perspective

Amin,

Acharya,

et al. 2024

Preprint

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Breast cancer (BC) is a leading cause of mortality in women worldwide, and its incidence and prognosis vary greatly, especially in low-income areas such as Asia and Africa, compared to America and Europe. This review examines the critical role of computer-aided diagnostic systems utilizing Deep Learning (DL) techniques in improving the precision of BC detection, which can help researchers and practitioners better understand the obstacles and emerging trends in the field. The comprehensive analysis and synthesis of the published articles is provided by the Preferred Reporting Items for Systematic Reviews and Meta- Analyses (PRISMA). Following several searches, 287 articles were found eligible for the assessment followed by data extraction and synthesis. This work provides insight into the pathology of BC and its corresponding mammographic appearance, a comprehensive analysis of publication trends, important contributors, and emerging themes using bibliometric techniques in addition to a thorough review of research articles. Focus maps were also created to identify and shed light on the body of knowledge. In contrast to other research, this review sheds light on the subtle identification of breast density, mass, and calcification from mammography images by emphasizing the critical evaluation of image pre- processing, augmentation, detection, segmentation, and classification techniques used from 2018 to 2023, further highlighting articles related to the Asian dataset. Furthermore, the review examines contributions made by authors, countries, and experimental datasets, providing insights into various approaches. Prospero registration: CRD42022478896

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“…Similarly, hardware constraints (storage, memory, computing capabilities, etc.) may limit the choice of usable models or further contribute to data transformation [26,[38][39][40][41]. Finally, difficulties related to the integration of AI tools in clinical practice (e.g., compatibility with PACS and RIS systems, characteristics of the imaging modalities) are part of these technical constraints [33,42,43].…”

Section: B3 Algorithm and Conceptionmentioning

confidence: 99%

“…Quality assurance (QA) (B4.3) Continuous monitoring of AI algorithms was recommended (n = 17) for continuous improvements or to prevent performance degradation over time due to changes, requiring to put in place QA procedures with adequate resources [40,43,46].…”

Section: B4 Evaluation and Validationmentioning

confidence: 99%

Barriers and facilitators of artificial intelligence conception and implementation for breast imaging diagnosis in clinical practice: a scoping review

Lokaj,

Pugliese,

Kinkel

et al. 2023

Eur Radiol

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Objective Although artificial intelligence (AI) has demonstrated promise in enhancing breast cancer diagnosis, the implementation of AI algorithms in clinical practice encounters various barriers. This scoping review aims to identify these barriers and facilitators to highlight key considerations for developing and implementing AI solutions in breast cancer imaging. Method A literature search was conducted from 2012 to 2022 in six databases (PubMed, Web of Science, CINHAL, Embase, IEEE, and ArXiv). The articles were included if some barriers and/or facilitators in the conception or implementation of AI in breast clinical imaging were described. We excluded research only focusing on performance, or with data not acquired in a clinical radiology setup and not involving real patients. Results A total of 107 articles were included. We identified six major barriers related to data (B1), black box and trust (B2), algorithms and conception (B3), evaluation and validation (B4), legal, ethical, and economic issues (B5), and education (B6), and five major facilitators covering data (F1), clinical impact (F2), algorithms and conception (F3), evaluation and validation (F4), and education (F5). Conclusion This scoping review highlighted the need to carefully design, deploy, and evaluate AI solutions in clinical practice, involving all stakeholders to yield improvement in healthcare. Clinical relevance statement The identification of barriers and facilitators with suggested solutions can guide and inform future research, and stakeholders to improve the design and implementation of AI for breast cancer detection in clinical practice. Key Points • Six major identified barriers were related to data; black-box and trust; algorithms and conception; evaluation and validation; legal, ethical, and economic issues; and education. • Five major identified facilitators were related to data, clinical impact, algorithms and conception, evaluation and validation, and education. • Coordinated implication of all stakeholders is required to improve breast cancer diagnosis with AI.

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Application of deep learning in the detection of breast lesions with four different breast densities

Abstract: This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Cited by 9 publications

References 26 publications

Reproducibility and Explainability of Deep Learning in Mammography: A Systematic Review of Literature

Reproducibility and Explainability of Deep Learning in Mammography: A Systematic Review of Literature

A Systematic Literature Review on Mammography: Deep Learning in Redefining Breast Cancer Diagnosis for the Asian Perspective

Barriers and facilitators of artificial intelligence conception and implementation for breast imaging diagnosis in clinical practice: a scoping review

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