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Mammography

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A computer-aided approach for automatic detection of breast masses in digital mammogram via spectral clustering and support vector machine.

Physical and engineering sciences in medicine
Breast cancer continues to be a widespread health concern all over the world. Mammography is an important method in the early detection of breast abnormalities. In recent years, using an automatic Computer-Aided Detection (CAD) system based on image ...

ResNet-SCDA-50 for Breast Abnormality Classification.

IEEE/ACM transactions on computational biology and bioinformatics
(Aim) Breast cancer is the most common cancer in women and the second most common cancer worldwide. With the rapid advancement of deep learning, the early stages of breast cancer development can be accurately detected by radiologists with the help of...

Deep learning of mammary gland distribution for architectural distortion detection in digital breast tomosynthesis.

Physics in medicine and biology
Computer aided detection (CADe) for breast lesions can provide an important reference for radiologists in breast cancer screening. Architectural distortion (AD) is a type of breast lesion that is difficult to detect. A majority of CADe methods focus ...

A tree-based multiclassification of breast tumor histopathology images through deep learning.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Worldwide, the burden of cancer is drastically increasing over the past few years. Among all types of cancers in women, breast cancer (BrC) is the main cause of unnatural deaths. For early diagnosis, histopathology (Hp) imaging is a gold standard for...

A deep learning model integrating mammography and clinical factors facilitates the malignancy prediction of BI-RADS 4 microcalcifications in breast cancer screening.

European radiology
OBJECTIVES: To investigate the value of full-field digital mammography-based deep learning (DL) in predicting malignancy of Breast Imaging Reporting and Data System (BI-RADS) 4 microcalcifications.

Retrospective analysis of the effect on interval cancer rate of adding an artificial intelligence algorithm to the reading process for two-dimensional full-field digital mammography.

Journal of medical screening
Interval cancers are a commonly seen problem in organized breast cancer screening programs and their rate is measured for quality assurance. Artificial intelligence algorithms have been proposed to improve mammography sensitivity, in which case it is...

Robust breast cancer detection in mammography and digital breast tomosynthesis using an annotation-efficient deep learning approach.

Nature medicine
Breast cancer remains a global challenge, causing over 600,000 deaths in 2018 (ref. ). To achieve earlier cancer detection, health organizations worldwide recommend screening mammography, which is estimated to decrease breast cancer mortality by 20-4...

Deep learning-based computer-aided diagnosis in screening breast ultrasound to reduce false-positive diagnoses.

Scientific reports
A major limitation of screening breast ultrasound (US) is a substantial number of false-positive biopsy. This study aimed to develop a deep learning-based computer-aided diagnosis (DL-CAD)-based diagnostic model to improve the differential diagnosis ...

SAP-cGAN: Adversarial learning for breast mass segmentation in digital mammogram based on superpixel average pooling.

Medical physics
PURPOSE: Breast mass segmentation is a prerequisite step in the use of computer-aided tools designed for breast cancer diagnosis and treatment planning. However, mass segmentation remains challenging due to the low contrast, irregular shapes, and fuz...