AIMC Topic: Breast

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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...

Imbalanced Breast Cancer Classification Using Transfer Learning.

IEEE/ACM transactions on computational biology and bioinformatics
Accurate breast cancer detection using automated algorithms remains a problem within the literature. Although a plethora of work has tried to address this issue, an exact solution is yet to be found. This problem is further exacerbated by the fact th...

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...

Deep learning in breast radiology: current progress and future directions.

European radiology
This review provides an overview of current applications of deep learning methods within breast radiology. The diagnostic capabilities of deep learning in breast radiology continue to improve, giving rise to the prospect that these methods may be int...

Deep Multi-Magnification Networks for multi-class breast cancer image segmentation.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Pathologic analysis of surgical excision specimens for breast carcinoma is important to evaluate the completeness of surgical excision and has implications for future treatment. This analysis is performed manually by pathologists reviewing histologic...

Automatic segmentation of ventricular volume by 3D ultrasonography in post haemorrhagic ventricular dilatation among preterm infants.

Scientific reports
To train, evaluate, and validate the application of a deep learning framework in three-dimensional ultrasound (3D US) for the automatic segmentation of ventricular volume in preterm infants with post haemorrhagic ventricular dilatation (PHVD). We tra...

Deep Learning Image Analysis of Benign Breast Disease to Identify Subsequent Risk of Breast Cancer.

JNCI cancer spectrum
BACKGROUND: New biomarkers of risk may improve breast cancer (BC) risk prediction. We developed a computational pathology method to segment benign breast disease (BBD) whole slide images into epithelium, fibrous stroma, and fat. We applied our method...

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...