Deep interpretation of mammographic images in breast cancer screening

乳腺癌筛查中乳腺X线摄影图像的深入解读

基本信息

批准号：
10165659
负责人：
Shandong Wu
金额：
$ 35.8万
依托单位：
UNIVERSITY OF PITTSBURGH AT PITTSBURGH
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-06-01 至 2023-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10165659
关键词：
Address Appearance Architecture Area Artificial Intelligence Back Behavior Benign Big Data Biological Markers Biopsy Breast Cancer Detection Breast Cancer Early Detection Breast Cancer Risk Factor Characteristics Clinic Clinical Computer Assisted Computers Data Data Analyses Data Reporting Data Set Decision Making Decision Modeling Digital Mammography Engineering General Population Goals Image Image Analysis Image Enhancement Individual Interneurons Knowledge Learning Lesion Machine Learning Malignant Neoplasms Mammographic screening Mammography Manuals Mathematics Methods Mining Modeling Network-based Neural Network Simulation Pattern Performance Process Psychological Transfer Radiology Specialty Reading Research Personnel Risk Risk Marker Shapes Side Techniques Testing Texture Training Translating Visualization software Weight Woman breast density breast imaging clinical application clinical decision-making clinical practice computerized convolutional neural network data mining deep learning deep neural network digital digital imaging imaging biomarker improved innovation insight interactive tool large datasets malignant breast neoplasm mortality neural network public health relevance radiologist risk prediction screening tool trait

项目摘要

Project Summary/Abstract Screening mammography has been shown effective in early detection of breast cancer and in reducing mortality. However, controversies and challenges still remain, with primary concerns on personal breast cancer risk prediction from mammographic parenchymal markers, high recall and benign biopsy rates, and improving radiologists’ clinical reading practices. Computerized methods have been developed in these regards, with the goal of providing computer assistance to radiologists in making clinical decisions. While successful, the accuracy of these methods is subject to appropriate data representation (i.e., image features) that requires strong feature engineering. A newly emerged artificial intelligence technique, called deep learning, represents a breakthrough in machine learning paradigms, and has revolutionized computer image analysis and many other applications in the past few years. Breast cancer screening yields a huge amount of mammogram data that requires in-depth interpretation to improve current clinical workup. The goal of this study is to develop and optimize a convolutional neural network (CNN)-based computational approach to improve mammographic imaging trait identification, analysis, and interpretation and to use this approach to address accurate breast cancer risk prediction and reduce false recall rates. This study will be the first to examine the effects of the revolutionary deep learning technique on performing in-depth interpretation of big screening mammogram data, aimed at improving clinical practice. The new risk biomarkers will contribute to providing more accurate risk prediction than currently available. The recall-decision model will help reduce false recalls (associated with potential benign biopsy results), and better understand radiologists’ reading behaviors. Overall, the CNN-based approach will optimize the clinical utility of screening mammography and has a high likelihood to translate to the clinic for breast cancer screening.

项目概要/摘要筛查性乳房X光检查已被证明可以有效地早期发现乳腺癌并减少乳腺癌的发生。然而，争议和挑战仍然存在，主要关注的是个人乳腺癌。根据乳房 X 线摄影实质标记物进行风险预测、高召回率和良性活检率，以及改进放射科医生的临床阅读实践在这些方面得到了发展。目标是为放射科医生做出临床决策提供计算机辅助。这些方法的准确性取决于适当的数据表示（即图像特征），这需要强特征工程代表了一种新出现的人工智能技术，称为深度学习。机器学习范式的突破，彻底改变了计算机图像分析和许多过去几年乳腺癌筛查的其他应用产生了大量的乳房X光检查数据。需要深入解释以改进当前的临床检查。这项研究的目标是开发和改进。优化基于卷积神经网络 (CNN) 的计算方法以改进乳腺 X 光检查成像特征识别、分析和解释，并使用这种方法来解决准确的乳房问题癌症风险预测和减少错误回忆率这项研究将是第一个检验其效果的研究。革命性的深度学习技术对大筛查乳房X光数据进行深入解释，旨在改善临床实践的新风险生物标志物将有助于提供更准确的风险。预测比当前可用的召回决策模型将有助于减少错误召回（与相关）。潜在的良性活检结果），并更好地了解放射科医生的阅读行为。该方法将优化筛查乳房X线照相术的临床效用，并且很可能转化为乳腺癌筛查诊所。

项目成果

期刊论文数量（21）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Inaccurate Labels in Weakly-Supervised Deep Learning: Automatic Identification and Correction and Their Impact on Classification Performance.

弱监督深度学习中的不准确标签：自动识别和纠正及其对分类性能的影响。

DOI：
发表时间：
2020-09
期刊：
影响因子：
0
作者：
Hao, Degan;Zhang, Lei;Sumkin, Jules;Mohamed, Aly;Wu, Shandong
通讯作者：
Wu, Shandong

A machine and human reader study on AI diagnosis model safety under attacks of adversarial images.

对抗性图像攻击下人工智能诊断模型安全性的机器和人类读者研究。

DOI：
发表时间：
2021-12-14
期刊：
影响因子：
16.6
作者：
Zhou, Qianwei;Zuley, Margarita;Guo, Yuan;Yang, Lu;Nair, Bronwyn;Vargo, Adrienne;Ghannam, Suzanne;Arefan, Dooman;Wu, Shandong
通讯作者：
Wu, Shandong

Machine learning for the prediction of pathologic pneumatosis intestinalis.

机器学习用于预测病理性肠积气。