Convolutional neural network approach for enhanced capture of breast parenchymal complexity patterns associated with breast cancer risk

Oustimov, Andrew; Gastounioti, Aimilia; Hsieh, M. Ani; Pantalone, Lauren; Conant, Emily F.; Kontos, Despina

doi:10.1117/12.2254506

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…Studies show that CNN methods that compare images from left and right breasts and also the craniocaudal (CC) and mediolateral-oblique (MLO) view of each breast can improve the accuracy of detection and reduce the false positives [55][56][57][58][59][60][61] . CNNs have been employed by radiologists to increase the accuracy of early detection of breast cancer by carrying out the required risk assessment applications [62][63][64][65][66][67][68][69][70][71].…”

Section: Convolutional Neural Network (Cnn) For Breast Cancer Screeningmentioning

confidence: 99%

Analytical Optimization of X-ray Mammography for Increased Benefits and Safety; using: Data Analytics, Electronics Engineering and Artificial Intelligence

Kalu¹

2023

J Health Statist Rep

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Objective: To analyse, understand and investigate common X-ray Mammography techniques, terminologies and properties; and how the X-ray Mammography systems can be optimized for a safer, more effective, more efficient, more energy efficient procedure for breast cancer diagnosis; using Deep Learning (DL)/ Artificial Intelligence (AI), Analytical, Electronic Engineering, tools and Methodologies. Methods: Using data and breast cancer image datasets from validated open source data stores; investigative and comparative analyses were carried out on common X-ray Mammography techniques in relation to breast cancer diagnosis and treatments for Women; as well as optimization analyses using Electronic engineering principles and Artificial Intelligence principles on the quality, intensity, radiation photon energy, HVL(Half Value Layer), Tube current-time product, Metal filters properties (e.g. K-edge energy cut off), and classification validation accuracy of the X-ray Mammography properties, procedure, processes and systems. The methodical and data-driven analyses were carried out using the following Data, Artificial Intelligence (AI) and Electronic Engineering, methodologies and algorithms: Data Analytics, Convolutional Neural Networks (CNN) in Machine Learning (ML) Engineering, and X-ray-Impedance Circuit Composite System Analysis

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Section: Convolutional Neural Network (Cnn) For Breast Cancer Screeningmentioning

confidence: 99%

Analytical Optimization of X-ray Mammography for Increased Benefits and Safety; using: Data Analytics, Electronics Engineering and Artificial Intelligence

Kalu¹

2023

J Health Statist Rep

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

et al. 2019

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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. CNNs also help radiologists providing more accurate diagnosis by delivering precise quantitative analysis of suspicious lesions. Results In this survey, we conducted a detailed review of the strengths, limitations, and performance of the most recent CNNs applications in analyzing MG images. It summarizes 83 research studies for applying CNNs on various tasks in mammography. It focuses on finding the best practices used in these research studies to improve the diagnosis accuracy. This survey also provides a deep insight into the architecture of CNNs used for various tasks. Furthermore, it describes the most common publicly available MG repositories and highlights their main features and strengths. Conclusions The mammography research community can utilize this survey as a basis for their current and future studies. The given comparison among common publicly available MG repositories guides the community to select the most appropriate database for their application(s). Moreover, this survey lists the best practices that improve the performance of CNNs including the pre-processing of images and the use of multi-view images. In addition, other listed techniques like transfer learning (TL), data augmentation, batch normalization, and dropout are appealing solutions to reduce overfitting and increase the generalization of the CNN models. Finally, this survey identifies the research challenges and directions that require further investigations by the community. Electronic supplementary material The online version of this article (10.1186/s12859-019-2823-4) contains supplementary material, which is available to authorized users.

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Convolutional neural network approach for enhanced capture of breast parenchymal complexity patterns associated with breast cancer risk

Cited by 2 publications

References 13 publications

Analytical Optimization of X-ray Mammography for Increased Benefits and Safety; using: Data Analytics, Electronics Engineering and Artificial Intelligence

Analytical Optimization of X-ray Mammography for Increased Benefits and Safety; using: Data Analytics, Electronics Engineering and Artificial Intelligence

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

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