Abstract
This chapter intends to demonstrate the clinical applications of Computer-Aided Diagnosis (CAD) systems based on deep learning algorithms while focusing on their IT infrastructure design. In comparison with traditional CAD systems that are mostly standalone applications designed to solve a particular task, we explain design choices of a cloud-based CAD platform that allows for running computational intense deep learning algorithms in a cost-efficient way. It also provides off-the-shelf solutions to collect, store, and secure data anywhere and anytime from various data sources, which is essential for training deep learning algorithms. In the end, we show the superior performance of using such CAD platform for analyzing medical imaging data of various modality before the conclusion.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ferlay, J., et al.: Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int. J. Cancer 136(5), E359–E386 (2015)
Girshick, R.: “Fast r-cnn”. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440–1448 (2015)
Gourd, E.: UK radiologist staffing crisis reaches critical levels. Lancet Oncol. 18(11), e651 (2017). ISSN: 1470-2045
He, K. et al., “Mask r-cnn”. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 2980–2988. IEEE (2017)
Kaggle Diabetic Retinopathy Challenge. https://www.kaggle.com/c/diabetic-retinopathy-detection
Liver Tumor Segmentation Challengee. https://competitions.codalab.org/competitions/17094
LUNA 2016 Nodule detection Grand Challenge. https://luna16.grand-challenge.org/
McWilliams, A., et al.: Probability of cancer in pulmonary nodules detected on first screening CT. N. Engl. J. Med. 369(10), 910–919 (2013)
Melendez, J. et al.: An automated tuberculosis screening strategy combining X-ray-based computer-aided detection and clinical information. Sci. Rep. 6, 25265 (2016)
National Lung Screening Trial Research Team: The national lung screening trial: overview and study design. Radiology 258(1), 243–253 (2011)
Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 234–241. Springer, Berlin (2015)
Siegel, R.L., Miller, K.D., Jemal, A.: Cancer statistics, 2015. CA: Cancer J. Clin. 65(1), 5–29 (2015)
Torre, L.A., et al.: Global cancer statistics, 2012. CA: Cancer J. Clin. 65(2), 87–108 (2015)
Zeng, H., et al.: Changing cancer survival in China during 2003–15: a pooled analysis of 17 population-based cancer registries. Lancet Glob. Health 6(5), e555–e567 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Cheng, G., He, L. (2020). Dr. Pecker: A Deep Learning-Based Computer-Aided Diagnosis System in Medical Imaging. In: Chen, YW., Jain, L. (eds) Deep Learning in Healthcare. Intelligent Systems Reference Library, vol 171. Springer, Cham. https://doi.org/10.1007/978-3-030-32606-7_12
Download citation
DOI: https://doi.org/10.1007/978-3-030-32606-7_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32605-0
Online ISBN: 978-3-030-32606-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)