AIMC Topic: Mammography

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Large Scale Semi-Automated Labeling of Routine Free-Text Clinical Records for Deep Learning.

Journal of digital imaging Feb 1, 2019
Breast cancer is a leading cause of cancer death among women in the USA. Screening mammography is effective in reducing mortality, but has a high rate of unnecessary recalls and biopsies. While deep learning can be applied to mammography, large-scale...
Humans Databases, Factual Electronic Health Records Female Breast Deep Learning Breast Neoplasms Image Interpretation, Computer-Assisted Mammography Middle Aged
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[Establishment of a deep feature-based classification model for distinguishing benign and malignant breast tumors on full-filed digital mammography].

Nan fang yi ke da xue xue bao = Journal of Southern Medical University Jan 30, 2019
OBJECTIVE: To develop a deep features-based model to classify benign and malignant breast lesions on full- filed digital mammography.
Mammography Breast Neoplasms Diagnosis, Computer-Assisted Deep Learning Humans Female
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New one-step model of breast tumor locating based on deep learning.

Journal of X-ray science and technology Jan 1, 2019
BACKGROUND: Breast cancer has the highest cancer prevalence rate among the women worldwide. Early detection of breast cancer is crucial for successful treatment and reducing cancer mortality rate. However, tumor detection of breast ultrasound (US) im...
Ultrasonography, Mammary Breast Female Breast Neoplasms Deep Learning Image Processing, Computer-Assisted Mammography Radiographic Image Interpretation, Computer-Assisted Humans Neural Networks, Computer Algorithms
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Breast mass detection and diagnosis using fused features with density.

Journal of X-ray science and technology Jan 1, 2019
BACKGROUND: The morbidity of breast cancer has been increased in these years and ranked the first of all female diseases. Computer-aided diagnosis techniques for mammograms can help radiologists find early breast lesions. In mammograms, the degree of...
Databases, Factual Female Aged Humans ROC Curve Unsupervised Machine Learning Mammography Radiographic Image Interpretation, Computer-Assisted Adult Breast Neoplasms Middle Aged Breast
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[Potential applications of deep learning-based technologies in Hungarian mammography].

Orvosi hetilap Jan 1, 2019
INTRODUCTION AND AIM: The technology, named 'deep learning' is the promising result of the last two decades of development in computer science. It poses an unavoidable challenge for medicine, how to understand, apply and adopt the - today not fully e...
Physician-Patient Relations Hungary Motivation Humans Deep Learning Artificial Intelligence User-Computer Interface Mammography
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Monitoring of Technology Adoption Using Web Content Mining of Location Information and Geographic Information Systems: A Case Study of Digital Breast Tomosynthesis.

JCO clinical cancer informatics Dec 1, 2018
PURPOSE: To our knowledge, integration of Web content mining of publicly available addresses with a geographic information system (GIS) has not been applied to the timely monitoring of medical technology adoption. Here, we explore the diffusion of a ...
Geographic Information Systems Geographic Mapping Humans Internet Mammography New England Data Mining Machine Learning Natural Language Processing Biomedical Technology Female
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Proposing New RadLex Terms by Analyzing Free-Text Mammography Reports.

Journal of digital imaging Oct 1, 2018
After years of development, the RadLex terminology contains a large set of controlled terms for the radiology domain, but gaps still exist. We developed a data-driven approach to discover new terms for RadLex by mining a large corpus of radiology rep...
Mammography Research Report Natural Language Processing Radiology Information Systems Humans Vocabulary, Controlled Female
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Understanding Clinical Mammographic Breast Density Assessment: a Deep Learning Perspective.

Journal of digital imaging Aug 1, 2018
Mammographic breast density has been established as an independent risk marker for developing breast cancer. Breast density assessment is a routine clinical need in breast cancer screening and current standard is using the Breast Imaging and Reportin...
Humans Area Under Curve Neural Networks, Computer Observer Variation Breast Density Retrospective Studies Databases, Factual Female Deep Learning United States Breast Neoplasms Cohort Studies Early Detection of Cancer ROC Curve Mammography
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Malignancy Detection on Mammography Using Dual Deep Convolutional Neural Networks and Genetically Discovered False Color Input Enhancement.

Journal of digital imaging Aug 1, 2017
Breast cancer is the most prevalent malignancy in the US and the third highest cause of cancer-related mortality worldwide. Regular mammography screening has been attributed with doubling the rate of early cancer detection over the past three decades...
Mammography Datasets as Topic Image Enhancement Humans Female Neural Networks, Computer Sensitivity and Specificity Breast Neoplasms Algorithms
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Grouped fuzzy SVM with EM-based partition of sample space for clustered microcalcification detection.

Technology and health care : official journal of the European Society for Engineering and Medicine Jul 20, 2017
BACKGROUND: Detection of clustered microcalcification (MC) from mammograms plays essential roles in computer-aided diagnosis for early stage breast cancer.
Support Vector Machine Breast Humans Algorithms Fuzzy Logic Models, Statistical Female Calcinosis Mammography Breast Neoplasms Radiographic Image Interpretation, Computer-Assisted
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