AIMC Topic: Breast

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Breast Mass Classification Using Diverse Contextual Information and Convolutional Neural Network.

Biosensors
Masses are one of the early signs of breast cancer, and the survival rate of women suffering from breast cancer can be improved if masses can be correctly identified as benign or malignant. However, their classification is challenging due to the simi...

Radiomics-based machine learning analysis and characterization of breast lesions with multiparametric diffusion-weighted MR.

Journal of translational medicine
BACKGROUND: This study aimed to evaluate the utility of radiomics-based machine learning analysis with multiparametric DWI and to compare the diagnostic performance of radiomics features and mean diffusion metrics in the characterization of breast le...

Comparing deep learning-based automatic segmentation of breast masses to expert interobserver variability in ultrasound imaging.

Computers in biology and medicine
Deep learning is a powerful tool that became practical in 2008, harnessing the power of Graphic Processing Unites, and has developed rapidly in image, video, and natural language processing. There are ongoing developments in the application of deep l...

Can artificial intelligence replace ultrasound as a complementary tool to mammogram for the diagnosis of the breast cancer?

The British journal of radiology
OBJECTIVE: To study the impact of artificial intelligence (AI) on the performance of mammogram with regard to the classification of the detected breast lesions in correlation to ultrasound-aided mammograms.

Automatic identification of triple negative breast cancer in ultrasonography using a deep convolutional neural network.

Scientific reports
Triple negative (TN) breast cancer is a subtype of breast cancer which is difficult for early detection and the prognosis is poor. In this paper, 910 benign and 934 malignant (110 TN and 824 NTN) B-mode breast ultrasound images were collected. A Resn...

BCHisto-Net: Breast histopathological image classification by global and local feature aggregation.

Artificial intelligence in medicine
Breast cancer among women is the second most common cancer worldwide. Non-invasive techniques such as mammograms and ultrasound imaging are used to detect the tumor. However, breast histopathological image analysis is inevitable for the detection of ...

Global processing provides malignancy evidence complementary to the information captured by humans or machines following detailed mammogram inspection.

Scientific reports
The information captured by the gist signal, which refers to radiologists' first impression arising from an initial global image processing, is poorly understood. We examined whether the gist signal can provide complementary information to data captu...

An Optimized Radiomics Model Based on Automated Breast Volume Scan Images to Identify Breast Lesions: Comparison of Machine Learning Methods: Comparison of Machine Learning Methods.

Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine
OBJECTIVES: To develop and test an optimized radiomics model based on multi-planar automated breast volume scan (ABVS) images to identify malignant and benign breast lesions.

Deep Learning for Automated Triaging of 4581 Breast MRI Examinations from the DENSE Trial.

Radiology
Background Supplemental screening with MRI has proved beneficial in women with extremely dense breasts. Most MRI examinations show normal anatomic and physiologic variation that may not require radiologic review. Thus, ways to triage these normal MRI...