AIMC Topic: Breast Neoplasms

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International evaluation of an AI system for breast cancer screening.

Nature
Screening mammography aims to identify breast cancer at earlier stages of the disease, when treatment can be more successful. Despite the existence of screening programmes worldwide, the interpretation of mammograms is affected by high rates of false...

Breast tumor classification through learning from noisy labeled ultrasound images.

Medical physics
PURPOSE: To train deep learning models to differentiate benign and malignant breast tumors in ultrasound images, we need to collect many training samples with clear labels. In general, biopsy results can be used as benign/malignant labels. However, m...

The implementation of natural language processing to extract index lesions from breast magnetic resonance imaging reports.

BMC medical informatics and decision making
BACKGROUND: There are often multiple lesions in breast magnetic resonance imaging (MRI) reports and radiologists usually focus on describing the index lesion that is most crucial to clinicians in determining the management and prognosis of patients. ...

Predicting breast cancer risk using personal health data and machine learning models.

PloS one
Among women, breast cancer is a leading cause of death. Breast cancer risk predictions can inform screening and preventative actions. Previous works found that adding inputs to the widely-used Gail model improved its ability to predict breast cancer ...

Machine Learning Methods for Computer-Aided Breast Cancer Diagnosis Using Histopathology: A Narrative Review.

Journal of medical imaging and radiation sciences
Histopathology is a method used for breast cancer diagnosis. Machine learning (ML) methods have achieved success for supervised learning tasks in the medical domain. In this article, we investigate the impact of ML for the diagnosis of breast cancer ...

Attention by Selection: A Deep Selective Attention Approach to Breast Cancer Classification.

IEEE transactions on medical imaging
Deep learning approaches are widely applied to histopathological image analysis due to the impressive levels of performance achieved. However, when dealing with high-resolution histopathological images, utilizing the original image as input to the de...

Multi-planar 3D breast segmentation in MRI via deep convolutional neural networks.

Artificial intelligence in medicine
Nowadays, Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI) has demonstrated to be a valid complementary diagnostic tool for early detection and diagnosis of breast cancer. However, without a CAD (Computer Aided Detection) system, manual...

Joint learning improves protein abundance prediction in cancers.

BMC biology
BACKGROUND: The classic central dogma in biology is the information flow from DNA to mRNA to protein, yet complicated regulatory mechanisms underlying protein translation often lead to weak correlations between mRNA and protein abundances. This is pa...

Photoelectrochemical detection of circulating tumor cells based on aptamer conjugated CuO as signal probe.

Biosensors & bioelectronics
In this work, a sensitive and reliable photoelectrochemical (PEC) biosensor was proposed based on hexagonal carbon nitride tubes (HCNT) as photoactive material for detection of circulating tumor cells (CTCs). Magnetic FeO nanospheres (MNs) and CuO na...

Automatic Identification of Breast Ultrasound Image Based on Supervised Block-Based Region Segmentation Algorithm and Features Combination Migration Deep Learning Model.

IEEE journal of biomedical and health informatics
Breast cancer is a high-incidence type of cancer for women. Early diagnosis plays a crucial role in the successful treatment of the disease and the effective reduction of deaths. In this paper, deep learning technology combined with ultrasound imagin...