AI Medical Compendium Topic:
Mammography

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Breast Cancer Diagnosis in Digital Breast Tomosynthesis: Effects of Training Sample Size on Multi-Stage Transfer Learning Using Deep Neural Nets.

IEEE transactions on medical imaging
In this paper, we developed a deep convolutional neural network (CNN) for the classification of malignant and benign masses in digital breast tomosynthesis (DBT) using a multi-stage transfer learning approach that utilized data from similar auxiliary...

Classification of Background Parenchymal Uptake on Molecular Breast Imaging Using a Convolutional Neural Network.

JCO clinical cancer informatics
PURPOSE: Background parenchymal uptake (BPU), which describes the level of radiotracer uptake in normal fibroglandular tissue on molecular breast imaging (MBI), has been identified as a breast cancer risk factor. Our objective was to develop and vali...

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

Journal of digital imaging
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...

[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
OBJECTIVE: To develop a deep features-based model to classify benign and malignant breast lesions on full- filed digital mammography.

New one-step model of breast tumor locating based on deep learning.

Journal of X-ray science and technology
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...

Breast mass detection and diagnosis using fused features with density.

Journal of X-ray science and technology
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...

[Potential applications of deep learning-based technologies in Hungarian mammography].

Orvosi hetilap
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...

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
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 ...

Proposing New RadLex Terms by Analyzing Free-Text Mammography Reports.

Journal of digital imaging
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...

Understanding Clinical Mammographic Breast Density Assessment: a Deep Learning Perspective.

Journal of digital imaging
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...