Titlebook: Deep Learning and Medical Applications; Jin Keun Seo Book 2023 The Editor(s) (if applicable) and The Author(s), under exclusive license to

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,Nonlinear Representation and Dimensionality Reduction,e been developed to process the high-dimensional data where the intrinsic dimensions are assumed to be much lower. In the very ideal case, data can be regressed linearly and DR can be performed by principal component analysis. This chapter explains the theories, principles, and practices of DR techn

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,Deep Learning Techniques for Medical Image Segmentation and Object Recognition,of the output diagnosis. Therefore, in order to safely utilize DL algorithms in the medical field, it is desirable to design the models to transparently explain the reason for making the output diagnosis rather than a black-box. For explainable DL, a systematic study is needed to rigorously analyze

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Deep Learning for Dental Cone-Beam Computed Tomography,P) algorithm. The presence of metallic objects in an imaging subject violates the model’s assumption that the CT sinogram data is equal to the Radon transform of an image. FBP ignores the polychromatic nature of the X-ray data ., which has nonlinear dependence on the distribution of the metallic obj

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,Artificial Intelligence for Digital Dentistry,n essential tool for almost all processes, including virtual treatment planning and on-screen simulation of surgical or dental treatment. Noting that the dental regions of 3D CT data do not have the level of resolution to be used directly for treatment, the jaw–tooth composite model, which accuratel

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https://doi.org/10.1007/978-3-658-15386-1of the output diagnosis. Therefore, in order to safely utilize DL algorithms in the medical field, it is desirable to design the models to transparently explain the reason for making the output diagnosis rather than a black-box. For explainable DL, a systematic study is needed to rigorously analyze

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