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

intensify

書目名稱Deep Learning and Medical Applications影響因子(影響力)




書目名稱Deep Learning and Medical Applications影響因子(影響力)學科排名




書目名稱Deep Learning and Medical Applications網絡公開度




書目名稱Deep Learning and Medical Applications網絡公開度學科排名




書目名稱Deep Learning and Medical Applications被引頻次




書目名稱Deep Learning and Medical Applications被引頻次學科排名




書目名稱Deep Learning and Medical Applications年度引用




書目名稱Deep Learning and Medical Applications年度引用學科排名




書目名稱Deep Learning and Medical Applications讀者反饋




書目名稱Deep Learning and Medical Applications讀者反饋學科排名

大方不好

978-981-99-1841-6The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor

TSH582

積習難改

https://doi.org/10.1007/978-3-658-15386-1mation related to its size and shape. Over the last few decades, many innovative methods of performing segmentation have been proposed, and these segmentation techniques are based on the basic recipes using thresholding and edge-based detection. Segmentation and classification in medical imaging are

superfluous

https://doi.org/10.1007/978-3-658-15386-1e number of aged people with artificial prostheses and metallic implants is swiftly increasing with the rapidly aging population. Metallic objects present in the CBCT field of view produce streaking artifacts that highly degrade the reconstructed CT images, resulting in a loss of information on the

Cryptic

https://doi.org/10.1007/978-3-658-15386-1at integrates 3D jaw–teeth–face data from various imaging devices such as cone-beam computerized tomography (CBCT), oral scanner, face scanner, 3D tracking devices, and others. Digital dentistry equipped with the AI-based integrated platform enables dentists to provide accurate diagnoses and treatme

Cryptic

granite

exacerbate

淺灘

Jin Keun SeoProvides an understanding of the interfaces between the model and other factors, and of clinical applications.Offers comprehensive, in-depth understanding of deep learning-based medical image‘analysis