AIMC Topic: Breast

Clear Filters Showing 301 to 310 of 627 articles

Saliency-guided deep learning network for automatic tumor bed volume delineation in post-operative breast irradiation.

Physics in medicine and biology
Efficient, reliable and reproducible target volume delineation is a key step in the effective planning of breast radiotherapy. However, post-operative breast target delineation is challenging as the contrast between the tumor bed volume (TBV) and nor...

Mammographic Surveillance After Breast-Conserving Therapy: Impact of Digital Breast Tomosynthesis and Artificial Intelligence-Based Computer-Aided Detection.

AJR. American journal of roentgenology
Postoperative mammograms present interpretive challenges due to postoperative distortion and hematomas. The application of digital breast tomosyn-thesis (DBT) and artificial intelligence-based computer-aided detection (AI-CAD) after breast-conservin...

Deep Learning Based Capsule Neural Network Model for Breast Cancer Diagnosis Using Mammogram Images.

Interdisciplinary sciences, computational life sciences
Breast cancer is a commonly occurring disease in women all over the world. Mammogram is an efficient technique used for screening and identification of abnormalities over the breast region. Earlier identification of breast cancer enhances the prognos...

A Data Set and Deep Learning Algorithm for the Detection of Masses and Architectural Distortions in Digital Breast Tomosynthesis Images.

JAMA network open
IMPORTANCE: Breast cancer screening is among the most common radiological tasks, with more than 39 million examinations performed each year. While it has been among the most studied medical imaging applications of artificial intelligence, the develop...

AI-enhanced breast imaging: Where are we and where are we heading?

European journal of radiology
Significant advances in imaging analysis and the development of high-throughput methods that can extract and correlate multiple imaging parameters with different clinical outcomes have led to a new direction in medical research. Radiomics and artific...

Distinguishing benign and malignant lesions on contrast-enhanced breast cone-beam CT with deep learning neural architecture search.

European journal of radiology
PURPOSE: To utilize a neural architecture search (NAS) approach to develop a convolutional neural network (CNN) method for distinguishing benign and malignant lesions on breast cone-beam CT (BCBCT).

An SVM approach towards breast cancer classification from H&E-stained histopathology images based on integrated features.

Medical & biological engineering & computing
Breast cancer is one among the most frequent reasons of women's death worldwide. Nowadays, healthcare informatics is mainly focussing on the classification of breast cancer images, due to the lethal nature of this cancer. There are chances of inter- ...

BCDnet: Parallel heterogeneous eight-class classification model of breast pathology.

PloS one
Breast cancer is the cancer with the highest incidence of malignant tumors in women, which seriously endangers women's health. With the help of computer vision technology, it has important application value to automatically classify pathological tiss...