Proceedings ArticleDOI
Deep Learning from Small Dataset for BI-RADS Density Classification of Mammography Images
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TLDR
A deep learning study of BI-RADS density classification using MIAS, based on a lightweight Convolutional Neural Networks (CNNs) architecture is presented, which has achieved a test accuracy of 83.6% on average and suggests that deep learning has the potential to address the small data problem in mammography.Abstract:
Mammography is a breast imaging technique that has been widely used in breast cancer diagnosis and screening. The Breast Imaging Reporting and Data System (BI-RADS) defines a six-point overall cancer risk scale from negative to highly suggestive of malignancy based on mammography, and also a four-point breast density based cancer risk scale. Automatic BI-RADS density classification of mammogram images is still a challenge. The current state of the art is about 80% on the MIAS (Mammogram Image Analysis Society) database. In this paper we present a deep learning study of BI-RADS density classification using MIAS, based on a lightweight Convolutional Neural Networks (CNNs) architecture. This is a small data problem as MIAS has only 322 images with ground truth, so we use image pre-processing and augmentation to solve the problem. Five-fold cross validation is used to evaluate the proposed approach, and has achieved a test accuracy of 83.6% on average. This suggests that deep learning has the potential to address the small data problem in mammography, which is prevalent in many medical image analysis tasks. The experience we have, especially in how to optimize the deep learning architecture, will benefit other researchers and medical practitioners.read more
Citations
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Image Augmentation Techniques for Mammogram Analysis
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Breast cancer detection using deep learning: Datasets, methods, and challenges ahead
TL;DR: A detailed review of the past research papers using Machine Learning, Deep Learning and Deep Reinforcement Learning for BC classification and detection is carried out in this article , where critical analysis of the research and findings already done to detect and classify BC using various imaging modalities including "Mammography", "Histopathology", "Ultrasound", "PET/CT", "MRI, and Thermography".
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Data augmentation for medical imaging: A systematic literature review
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Dense Tissue Pattern Characterization Using Deep Neural Network
Indrajeet Kumar,Abhishek Kumar,V. D. Ambeth Kumar,R. Kannan,Vrince Vimal,Kamred Udham Singh,Mufti Mahmud +6 more
TL;DR: In this paper , the authors proposed a dense tissue pattern characterization framework using deep neural network, which achieved an accuracy of 91.9% and kappa coefficient value of 0.839 using ResNet-18 model.
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Thyroid nodules risk stratification through deep learning based on ultrasound images.
Ziyu Bai,Luchen Chang,Ruiguo Yu,Xuewei Li,Xi Wei,Mei Yu,Zhiqiang Liu,Jie Gao,Jialin Zhu,Yulin Zhang,Shuaijie Wang,Zhuo Zhang +11 more
TL;DR: This work provides a way to automate the risk stratification of thyroid nodules based on deep integration of deep learning and clinical experience, and can effectively avoid missed diagnosis and misdiagnosis caused by the difference of observers so as to assist doctors to improve efficiency and diagnosis rate.
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