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Three Aspects on Using Convolutional Neural Networks for Computer-Aided Detection in Medical Imaging

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Book cover Deep Learning and Convolutional Neural Networks for Medical Image Computing

Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

Abstract

Deep convolutional neural networks (CNNs) enable learning trainable, highly representative and hierarchical image feature from sufficient training data which makes rapid progress in computer vision possible. There are currently three major techniques that successfully employ CNNs to medical image classification: training the CNN from scratch, using off-the-shelf pretrained CNN features, and transfer learning , i.e., fine-tuning CNN models pretrained from natural image dataset (such as large-scale annotated natural image database: ImageNet) to medical image tasks. In this chapter, we exploit three important factors of employing deep convolutional neural networks to computer-aided detection problems. First, we exploit and evaluate several different CNN architectures including from shallower to deeper CNNs: classical CifarNet, to recent AlexNet and state-of-the-art GoogLeNet and their variants. The studied models contain five thousand to 160 million parameters and vary in the numbers of layers. Second, we explore the influence of dataset scales and spatial image context configurations on medical image classification performance. Third, when and why transfer learning from the pretrained ImageNet CNN models (via fine-tuning) can be useful for medical imaging tasks are carefully examined. We study two specific computer-aided detection (CADe) problems, namely thoracoabdominal lymph node (LN) detection and interstitial lung disease (ILD) classification. We achieve the state-of-the-art performance on the mediastinal LN detection and report the first fivefold cross-validation classification results on predicting axial CT slices with ILD categories. Our extensive quantitative evaluation, CNN model analysis, and empirical insights can be helpful to the design of high-performance CAD systems for other medical imaging tasks, without loss of generality.

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Notes

  1. 1.

    This can be achieved by segmenting the lung using simple label fusion methods [46]. First, we overlay the target image slice with the average lung mask among the training folds. Second, we perform simple morphology operations to obtain the lung boundary.

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Author information

Authors and Affiliations

  1. Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, 20837, USA

    Hoo-Chang Shin, Holger R. Roth, Mingchen Gao, Le Lu, Ziyue Xu, Isabella Nogues, Jianhua Yao, Daniel Mollura & Ronald M. Summers

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  1. Hoo-Chang Shin
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  2. Holger R. Roth
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  3. Mingchen Gao
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  4. Le Lu
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  5. Ziyue Xu
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  6. Isabella Nogues
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  7. Jianhua Yao
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  8. Daniel Mollura
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  9. Ronald M. Summers
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Correspondence to Hoo-Chang Shin .

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Editors and Affiliations

  1. NIH Clinical Center, Bethesda, Maryland, USA

    Le Lu

  2. Siemens Healthcare Technology Center, Princeton, New Jersey, USA

    Yefeng Zheng

  3. University of Adelaide, Adelaide, South Australia, Australia

    Gustavo Carneiro

  4. University of Florida, Gainesville, Florida, USA

    Lin Yang

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Shin, HC. et al. (2017). Three Aspects on Using Convolutional Neural Networks for Computer-Aided Detection in Medical Imaging. In: Lu, L., Zheng, Y., Carneiro, G., Yang, L. (eds) Deep Learning and Convolutional Neural Networks for Medical Image Computing. Advances in Computer Vision and Pattern Recognition. Springer, Cham. https://doi.org/10.1007/978-3-319-42999-1_8

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  • DOI: https://doi.org/10.1007/978-3-319-42999-1_8

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