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Computerized Detection of Lesions in Diagnostic Images with Early Deep Learning Models

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Machine and Deep Learning in Oncology, Medical Physics and Radiology

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Abstract

Computer-aided detection (CADe) and diagnosis (CADx) have been active research areas in medical imaging. As imaging technologies advance, a large number of medical images are produced which physicians/radiologists must read. They may overlook lesions from such a large number of medical images. Consequently, CADe that provides suspicious lesions with radiologists/physicians is developed and becoming indispensable in their decision-making to prevent them from overlooking lesions. Machine learning (ML) plays an essential role in CADe and CADx, because lesions and organs in medical images may be too complex to be represented accurately by a simple equation; modeling of such complex objects often requires a number of parameters that have to be determined by data. In this chapter, ML techniques (feature-based ML) and early deep learning models (called massive-training artificial neural networks (MTANNs)) used in CADe and CADx schemes for lung nodules in chest radiography and thoracic CT and those for the detection of polyps in CTC are described.

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Acknowledgments

This work would not have been possible without the help and support of countless people. The author is grateful to all members in the Suzuki laboratory, i.e., postdoctoral scholars, computer scientists, visiting scholars/professors, medical students, graduate/undergraduate students, research technicians, research volunteers, and support staff, in the Department of Radiology at the University of Chicago, in the Medical Imaging Research Center at the Illinois Institute of Technology, for their invaluable assistance in the studies, to colleagues and collaborators for their valuable suggestions. CAD technologies, MTANNs technologies, the bone separation technology, and their source code developed at the University of Chicago have been licensed to companies including R2 Technology (Hologic), Riverain Medical (Riverain Technologies), Median Technologies, and AlgoMedica.

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    Kenji Suzuki

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  1. Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, USA

    Issam El Naqa

  2. Department of Radiation Oncology, Virginia Commonwealth University, Richmond, VA, USA

    Martin J. Murphy

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Suzuki, K. (2022). Computerized Detection of Lesions in Diagnostic Images with Early Deep Learning Models. In: El Naqa, I., Murphy, M.J. (eds) Machine and Deep Learning in Oncology, Medical Physics and Radiology. Springer, Cham. https://doi.org/10.1007/978-3-030-83047-2_9

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