AIMC Topic: Breast Neoplasms

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A novel multi-view deep learning approach for BI-RADS and density assessment of mammograms.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Advanced deep learning (DL) algorithms may predict the patient's risk of developing breast cancer based on the Breast Imaging Reporting and Data System (BI-RADS) and density standards. Recent studies have suggested that the combination of multi-view ...

Comparative Analysis of Current Deep Learning Networks for Breast Lesion Segmentation in Ultrasound Images.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Automatic lesion segmentation in breast ultrasound (BUS) images aids in the diagnosis of breast cancer, the most common type of cancer in women. Accurate lesion segmentation in ultrasound images is a challenging task due to speckle noise, artifacts, ...

RUBY: Natural Language Processing of French Electronic Medical Records for Breast Cancer Research.

JCO clinical cancer informatics
PURPOSE: Electronic medical records are a valuable source of information about patients' clinical status but are often free-text documents that require laborious manual review to be exploited. Techniques from computer science have been investigated, ...

Detection of live breast cancer cells in bright-field microscopy images containing white blood cells by image analysis and deep learning.

Journal of biomedical optics
SIGNIFICANCE: Circulating tumor cells (CTCs) are important biomarkers for cancer management. Isolated CTCs from blood are stained to detect and enumerate CTCs. However, the staining process is laborious and moreover makes CTCs unsuitable for drug tes...

Scaling multi-instance support vector machine to breast cancer detection on the BreaKHis dataset.

Bioinformatics (Oxford, England)
MOTIVATION: Breast cancer is a type of cancer that develops in breast tissues, and, after skin cancer, it is the most commonly diagnosed cancer in women in the United States. Given that an early diagnosis is imperative to prevent breast cancer progre...

Comparison of MetaMap, cTAKES, SIFR, and ECMT to Annotate Breast Cancer Patient Summaries.

Studies in health technology and informatics
Most clinical texts including breast cancer patient summaries (BCPSs) are elaborated as narrative documents difficult to process by decision support systems. Annotators have been developed to extract the relevant content of such documents, e.g., Meta...

Using Machine Learning and Deep Learning Methods to Predict the Complexity of Breast Cancer Cases.

Studies in health technology and informatics
In many countries, the management of cancer patients must be discussed in multidisciplinary tumor boards (MTBs). These meetings have been introduced to provide a collaborative and multidisciplinary approach to cancer care. However, the benefits of MT...

Simple Linear Cancer Risk Prediction Models With Novel Features Outperform Complex Approaches.

JCO clinical cancer informatics
PURPOSE: The ability to accurately predict an individual's risk for cancer is critical to the implementation of precision prevention measures. Current cancer risk predictions are frequently made with simple models that use a few proven risk factors, ...

An app to classify a 5-year survival in patients with breast cancer using the convolutional neural networks (CNN) in Microsoft Excel: Development and usability study.

Medicine
BACKGROUND: Breast cancer (BC) is the most common malignant cancer in women. A predictive model is required to predict the 5-year survival in patients with BC (5YSPBC) and improve the treatment quality by increasing their survival rate. However, no r...