Journal ArticleDOI
A deep learning framework for supporting the classification of breast lesions in ultrasound images.
Seokmin Han,Ho-kyung Kang,Ja-Yeon Jeong,Moon-Ho Park,Wonsik Kim,Won-Chul Bang,Yeong Kyeong Seong +6 more
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TLDR
The proposed method can work in tandem with human radiologists to improve performance, which is a fundamental purpose of computer-aided diagnosis.Abstract:
In this research, we exploited the deep learning framework to differentiate the distinctive types of lesions and nodules in breast acquired with ultrasound imaging. A biopsy-proven benchmarking dataset was built from 5151 patients cases containing a total of 7408 ultrasound breast images, representative of semi-automatically segmented lesions associated with masses. The dataset comprised 4254 benign and 3154 malignant lesions. The developed method includes histogram equalization, image cropping and margin augmentation. The GoogLeNet convolutionary neural network was trained to the database to differentiate benign and malignant tumors. The networks were trained on the data with augmentation and the data without augmentation. Both of them showed an area under the curve of over 0.9. The networks showed an accuracy of about 0.9 (90%), a sensitivity of 0.86 and a specificity of 0.96. Although target regions of interest (ROIs) were selected by radiologists, meaning that radiologists still have to point out the location of the ROI, the classification of malignant lesions showed promising results. If this method is used by radiologists in clinical situations it can classify malignant lesions in a short time and support the diagnosis of radiologists in discriminating malignant lesions. Therefore, the proposed method can work in tandem with human radiologists to improve performance, which is a fundamental purpose of computer-aided diagnosis.read more
Citations
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Journal ArticleDOI
Deep Learning in Medical Ultrasound Analysis: A Review
TL;DR: Several popular deep learning architectures are briefly introduced, and their applications in various specific tasks in US image analysis, such as classification, detection, and segmentation are discussed.
Journal ArticleDOI
Convolutional Neural Networks for Radiologic Images: A Radiologist’s Guide
TL;DR: An introduction to deep learning technology is provided and the stages that are entailed in the design process of deep learning radiology research are presented and the results of a survey of the application of convolutional neural networks to radiologic imaging are detailed.
Journal ArticleDOI
Deep and machine learning techniques for medical imaging-based breast cancer: A comprehensive review
TL;DR: This study aims at presenting a review that shows the new applications of machine learning and deep learning technology for detecting and classifying breast cancer and provides an overview of progress and the future trends and challenges in the classification and detection of breast cancer.
Journal ArticleDOI
Computer?aided diagnosis of breast ultrasound images using ensemble learning from convolutional neural networks
TL;DR: Results indicated different image content representations that affect the prediction performance of the CAD system, more image information improves the predictions performance, and the tumor shape feature can improve the diagnostic effect.
Journal ArticleDOI
Machine Learning in Ultrasound Computer-Aided Diagnostic Systems: A Survey.
TL;DR: This paper summarized the research which focuses on the ultrasound CAD system utilizing machine learning technology in recent years and introduced the major feature and the classifier employed by the traditional ultrasound CAD and the deep learning ultrasound CAD.
References
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Proceedings ArticleDOI
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Christian Szegedy,Wei Liu,Yangqing Jia,Pierre Sermanet,Scott Reed,Dragomir Anguelov,Dumitru Erhan,Vincent Vanhoucke,Andrew Rabinovich +8 more
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TL;DR: Caffe as discussed by the authors is a BSD-licensed C++ library with Python and MATLAB bindings for training and deploying general-purpose convolutional neural networks and other deep models efficiently on commodity architectures.
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Representation Learning: A Review and New Perspectives
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Caffe: Convolutional Architecture for Fast Feature Embedding
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TL;DR: Caffe provides multimedia scientists and practitioners with a clean and modifiable framework for state-of-the-art deep learning algorithms and a collection of reference models for training and deploying general-purpose convolutional neural networks and other deep models efficiently on commodity architectures.