AIMC Topic: Breast

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C-Net: Cascaded convolutional neural network with global guidance and refinement residuals for breast ultrasound images segmentation.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Breast lesions segmentation is an important step of computer-aided diagnosis system. However, speckle noise, heterogeneous structure, and similar intensity distributions bring challenges for breast lesion segmentation.

The potential of predictive and prognostic breast MRI (P2-bMRI).

European radiology experimental
Magnetic resonance imaging (MRI) is an important part of breast cancer diagnosis and multimodal workup. It provides unsurpassed soft tissue contrast to analyse the underlying pathophysiology, and it is adopted for a variety of clinical indications. P...

Breast cancer patient characterisation and visualisation using deep learning and fisher information networks.

Scientific reports
Breast cancer is the most commonly diagnosed female malignancy globally, with better survival rates if diagnosed early. Mammography is the gold standard in screening programmes for breast cancer, but despite technological advances, high error rates a...

Sensor-Based Automated Detection of Electrosurgical Cautery States.

Sensors (Basel, Switzerland)
In computer-assisted surgery, it is typically required to detect when the tool comes into contact with the patient. In activated electrosurgery, this is known as the . By continuously tracking the electrosurgical tools' location using a navigation sy...

SAFNet: A deep spatial attention network with classifier fusion for breast cancer detection.

Computers in biology and medicine
Breast cancer is a top dangerous killer for women. An accurate early diagnosis of breast cancer is the primary step for treatment. A novel breast cancer detection model called SAFNet is proposed based on ultrasound images and deep learning. We employ...

A multi-level feature-fusion-based approach to breast histopathological image classification.

Biomedical physics & engineering express
Previously, convolutional neural networks mostly used deep semantic feature information obtained from several convolutions for image classification. Such deep semantic features have a larger receptive field, and the features extracted are more effect...

A Convolutional Neural Network and Graph Convolutional Network Based Framework for Classification of Breast Histopathological Images.

IEEE journal of biomedical and health informatics
The spatial correlation among different tissue components is an essential characteristic for diagnosis of breast cancers based on histopathological images. Graph convolutional network (GCN) can effectively capture this spatial feature representation,...

Deep Learning-Based Real-Time Discriminate Correlation Analysis for Breast Cancer Detection.

BioMed research international
Breast cancer is the most common cancer in women, and the breast mass recognition model can effectively assist doctors in clinical diagnosis. However, the scarcity of medical image samples makes the recognition model prone to overfitting. A breast ma...

Using deep learning to safely exclude lesions with only ultrafast breast MRI to shorten acquisition and reading time.

European radiology
OBJECTIVES: To investigate the feasibility of automatically identifying normal scans in ultrafast breast MRI with artificial intelligence (AI) to increase efficiency and reduce workload.

YOLO-LOGO: A transformer-based YOLO segmentation model for breast mass detection and segmentation in digital mammograms.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Both mass detection and segmentation in digital mammograms play a crucial role in early breast cancer detection and treatment. Furthermore, clinical experience has shown that they are the upstream tasks of pathological class...