AIMC Topic: Breast

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Impact of artificial intelligence support on accuracy and reading time in breast tomosynthesis image interpretation: a multi-reader multi-case study.

European radiology
OBJECTIVES: Digital breast tomosynthesis (DBT) increases sensitivity of mammography and is increasingly implemented in breast cancer screening. However, the large volume of images increases the risk of reading errors and reading time. This study aims...

Deep Learning-Based Artificial Intelligence for Mammography.

Korean journal of radiology
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 i...

AI-based Strategies to Reduce Workload in Breast Cancer Screening with Mammography and Tomosynthesis: A Retrospective Evaluation.

Radiology
Background The workflow of breast cancer screening programs could be improved given the high workload and the high number of false-positive and false-negative assessments. Purpose To evaluate if using an artificial intelligence (AI) system could redu...

Tumor classification in automated breast ultrasound (ABUS) based on a modified extracting feature network.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
People can get consistent Automated Breast Ultrasound (ABUS) images due to the imaging mechanism of scanning. Therefore, it has unique advantages in breast tumor classification using artificial intelligence technology. This paper proposes a method fo...

Application of deep learning to establish a diagnostic model of breast lesions using two-dimensional grayscale ultrasound imaging.

Clinical imaging
PURPOSE: There are currently few specific artificial intelligence (AI) studies for Breast Imaging Reporting and Data System (BI-RADS) category 4A lesions. This study aimed to establish an AI diagnostic model of breast lesions using two-dimensional gr...

Deep learning reconstruction of digital breast tomosynthesis images for accurate breast density and patient-specific radiation dose estimation.

Medical image analysis
The two-dimensional nature of mammography makes estimation of the overall breast density challenging, and estimation of the true patient-specific radiation dose impossible. Digital breast tomosynthesis (DBT), a pseudo-3D technique, is now commonly us...

Risks of feature leakage and sample size dependencies in deep feature extraction for breast mass classification.

Medical physics
PURPOSE: Transfer learning is commonly used in deep learning for medical imaging to alleviate the problem of limited available data. In this work, we studied the risk of feature leakage and its dependence on sample size when using pretrained deep con...

Estimation of tumor parameters using neural networks for inverse bioheat problem.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Some types of cancer cause rapid cell growth, while others cause cells to grow and divide at a slower rate. Certain forms of cancer result in visible growths called tumors. This work proposes an inverse estimation of the siz...

Applying deep learning in digital breast tomosynthesis for automatic breast cancer detection: A review.

Medical image analysis
The relatively recent reintroduction of deep learning has been a revolutionary force in the interpretation of diagnostic imaging studies. However, the technology used to acquire those images is undergoing a revolution itself at the very same time. Di...

Artificial intelligence for the real world of breast screening.

European journal of radiology
Breast cancer screening with mammography reduces mortality in the women who attend by detecting high risk cancer early. It is far from perfect with variations in both sensitivity for the detection of cancer and very wide variations in specificity, le...