Journal ArticleDOI
Medical breast ultrasound image segmentation by machine learning.
TLDR
This paper proposes to use convolutional neural networks (CNNs) for segmenting breast ultrasound images into four major tissues: skin, fibroglandular tissue, mass, and fatty tissue, on three‐dimensional (3D) Breast ultrasound images.About:
This article is published in Ultrasonics.The article was published on 2019-01-01. It has received 151 citations till now. The article focuses on the topics: Breast ultrasound & Breast cancer.read more
Citations
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Journal ArticleDOI
Deep learning approaches to biomedical image segmentation
TL;DR: In this review, the basics of deep learning methods are discussed along with an overview of successful implementations involving image segmentation for different medical applications and the future need for further improvements is pointed out.
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
Breast mass classification in sonography with transfer learning using a deep convolutional neural network and color conversion
Michal Byra,Michal Byra,Michael Galperin,Haydee Ojeda-Fournier,Linda K. Olson,Mary O'Boyle,Christopher Comstock,Michael P. Andre +7 more
TL;DR: The concept of the matching layer is generalizable and can be used to improve the overall performance of the transfer learning techniques using deep convolutional neural networks.
Journal ArticleDOI
Machine Learning Techniques for Biomedical Image Segmentation: An Overview of Technical Aspects and Introduction to State-of-Art Applications
Hyunseok Seo,Masoud Badiei Khuzani,Varun Vasudevan,Charles Huang,Hongyi Ren,Ruoxiu Xiao,Xiao Jia,Lei Xing +7 more
TL;DR: This review article highlights the imperative role of machine learning algorithms in enabling efficient and accurate segmentation in the field of medical imaging and discusses several challenges related to the training of different machine learning models, and presents some heuristics to address those challenges.
Journal ArticleDOI
An RDAU-NET model for lesion segmentation in breast ultrasound images.
TL;DR: The experimental results illustrate that the proposed RDAU-NET model can accurately segment breast lesions when compared to other deep learning models and thus has a good prospect for clinical diagnosis.
References
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Proceedings ArticleDOI
Fully convolutional networks for semantic segmentation
TL;DR: The key insight is to build “fully convolutional” networks that take input of arbitrary size and produce correspondingly-sized output with efficient inference and learning.
Journal ArticleDOI
Cancer statistics, 2018
TL;DR: The combined cancer death rate dropped continuously from 1991 to 2015 by a total of 26%, translating to approximately 2,378,600 fewer cancer deaths than would have been expected if death rates had remained at their peak.
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Cancer statistics in China, 2015
Wanqing Chen,Rongshou Zheng,Peter D. Baade,Siwei Zhang,Hongmei Zeng,Freddie Bray,Ahmedin Jemal,Xue Qin Yu,Jie He +8 more
TL;DR: Many of the estimated cancer cases and deaths can be prevented through reducing the prevalence of risk factors, while increasing the effectiveness of clinical care delivery, particularly for those living in rural areas and in disadvantaged populations.
Posted Content
Fully Convolutional Networks for Semantic Segmentation
TL;DR: It is shown that convolutional networks by themselves, trained end- to-end, pixels-to-pixels, improve on the previous best result in semantic segmentation.
Journal ArticleDOI
Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation
Konstantinos Kamnitsas,Christian Ledig,Virginia F. J. Newcombe,Joanna P. Simpson,Andrew D. Kane,David K. Menon,Daniel Rueckert,Ben Glocker +7 more
TL;DR: An efficient and effective dense training scheme which joins the processing of adjacent image patches into one pass through the network while automatically adapting to the inherent class imbalance present in the data, and improves on the state-of-the‐art for all three applications.