Titlebook: Machine Learning in Dentistry; Ching-Chang Ko,Dinggang Shen,Li Wang Book 2021 Springer Nature Switzerland AG 2021 Dental big data.Digital

Antagonist

Segmenting Bones from Brain MRI via Generative Adversarial Learninger modality, resulting in a new MRI-CT pair of which the anatomical structure information are supposed to be consistent. In this way, the bone annotations (labels) from CT modality are implicitly transferred to the MRI modality to train the segmentation sub-network. We train the model in a semi-supe

改良

Sparse Dictionary Learning for 3D Craniomaxillofacial Skeleton Estimation Based on 2D Face Photograpated. After that, the initial estimation is refined by applying nonrigid surface matching between the initially estimated shape and the patient’s posttraumatic bone based on the adaptive-focus deformable shape model (AFDSM). Furthermore, a statistical shape model, built from training normal subjects

生命

Machine Learning for Facial Recognition in Orthodonticsial images for orthodontic diagnostic purposes and a system that automatically identifies cephalometric landmarks using landmark-dependent multi-scale patches. Such AI systems enable orthodontists to understand patients’ cranio-facial morphological problems correctly and quickly and can even help in

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Machine/Deep Learning for Performing Orthodontic Diagnoses and Treatment Planninge techniques for detecting regularity and specificity in observational data. In this chapter, we will introduce several AI systems that have been used to derive orthodontic diagnoses and develop treatment plans in the past. We will then introduce a newly developed AI system that uses natural languag

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JAUNT

哑巴

theta-waves

Bumptious

filial