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Breast

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BCR-Net: A deep learning framework to predict breast cancer recurrence from histopathology images.

PloS one
Breast cancer is the most common malignancy in women, with over 40,000 deaths annually in the United States alone. Clinicians often rely on the breast cancer recurrence score, Oncotype DX (ODX), for risk stratification of breast cancer patients, by u...

Breast Tumor Classification using Short-ResNet with Pixel-based Tumor Probability Map in Ultrasound Images.

Ultrasonic imaging
Breast cancer is the most common form of cancer and is still the second leading cause of death for women in the world. Early detection and treatment of breast cancer can reduce mortality rates. Breast ultrasound is always used to detect and diagnose ...

Multi CNN based automatic detection of mitotic nuclei in breast histopathological images.

Computers in biology and medicine
In breast cancer diagnosis, the number of mitotic cells in a specific area is an important measure. It indicates how far the tumour has spread, which has consequences for forecasting the aggressiveness of cancer. Mitosis counting is a time-consuming ...

A novel deep learning model for breast lesion classification using ultrasound Images: A multicenter data evaluation.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: Breast cancer is one of the major reasons of death due to cancer in women. Early diagnosis is the most critical key for disease screening, control, and reducing mortality. A robust diagnosis relies on the correct classification of breast les...

An Automatic Breast Tumor Detection and Classification including Automatic Tumor Volume Estimation Using Deep Learning Technique.

Asian Pacific journal of cancer prevention : APJCP
OBJECTIVE: This study aims to develop automatic breast tumor detection and classification including automatic tumor volume estimation using deep learning techniques based on computerized analysis of breast ultrasound images. When the skill levels of ...

Clinical applications of deep learning in breast MRI.

Biochimica et biophysica acta. Reviews on cancer
Deep learning (DL) is one of the most powerful data-driven machine-learning techniques in artificial intelligence (AI). It can automatically learn from raw data without manual feature selection. DL models have led to remarkable advances in data extra...

Prediction of malignancy upgrade rate in high-risk breast lesions using an artificial intelligence model: a retrospective study.

Diagnostic and interventional radiology (Ankara, Turkey)
PURPOSE: High-risk breast lesions (HRLs) are associated with future risk of breast cancer. Considering the pathological subtypes, malignancy upgrade rate differs according to each subtype and depends on various factors such as clinical and radiologic...

Artificial intelligence breast ultrasound and handheld ultrasound in the BI-RADS categorization of breast lesions: A pilot head to head comparison study in screening program.

Frontiers in public health
BACKGROUND: Artificial intelligence breast ultrasound diagnostic system (AIBUS) has been introduced as an alternative approach for handheld ultrasound (HHUS), while their results in BI-RADS categorization has not been compared.

Deep learning for x-ray scatter correction in dedicated breast CT.

Medical physics
BACKGROUND: Accurate correction of x-ray scatter in dedicated breast computed tomography (bCT) imaging may result in improved visual interpretation and is crucial to achieve quantitative accuracy during image reconstruction and analysis.