標(biāo)題: Titlebook: Machine Learning in Dentistry; Ching-Chang Ko,Dinggang Shen,Li Wang Book 2021 Springer Nature Switzerland AG 2021 Dental big data.Digital [打印本頁] 作者: 手套 時間: 2025-3-21 16:43
書目名稱Machine Learning in Dentistry影響因子(影響力)
書目名稱Machine Learning in Dentistry影響因子(影響力)學(xué)科排名
書目名稱Machine Learning in Dentistry網(wǎng)絡(luò)公開度
書目名稱Machine Learning in Dentistry網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Machine Learning in Dentistry被引頻次
書目名稱Machine Learning in Dentistry被引頻次學(xué)科排名
書目名稱Machine Learning in Dentistry年度引用
書目名稱Machine Learning in Dentistry年度引用學(xué)科排名
書目名稱Machine Learning in Dentistry讀者反饋
書目名稱Machine Learning in Dentistry讀者反饋學(xué)科排名
作者: novelty 時間: 2025-3-21 22:00 作者: ligature 時間: 2025-3-22 01:23 作者: 詢問 時間: 2025-3-22 04:48
Hannah H. Deng,Li Wang,Yi Ren,Jaime Gateno,Zhen Tang,Ken-Chung Chen,Chunfeng Lian,Steve Guofang Shen作者: SPER 時間: 2025-3-22 11:34 作者: CANON 時間: 2025-3-22 14:11
Shankar Rengasamy Venugopalan,Mohammed H. Elnagar,Deepti S. Karhade,Veerasathpurush Allareddy作者: lesion 時間: 2025-3-22 18:28
Alonso Carrasco-Labra,Olivia Urquhart,Heiko Spallek作者: Oratory 時間: 2025-3-22 21:15
Di Wu,Deepti S. Karhade,Malvika Pillai,Min-Zhi Jiang,Le Huang,Gang Li,Hunyong Cho,Jeff Roach,Yun Li,作者: 節(jié)省 時間: 2025-3-23 03:04 作者: 恃強(qiáng)凌弱的人 時間: 2025-3-23 07:26
Machine Learning for CBCT Segmentation of Craniomaxillofacial Bony Structuresf expert-segmented CBCT images as the atlases to perform majority voting for the estimation of the initial segmentation probability maps for an input CBCT image. Guided by the contextual prior provided by the initial probability maps, an auto-context random forest is constructed, which uses the appe作者: Antagonist 時間: 2025-3-23 13:08
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作者: 改良 時間: 2025-3-23 16:53
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作者: 生命 時間: 2025-3-23 21:35
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作者: noxious 時間: 2025-3-24 01:26
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作者: 無思維能力 時間: 2025-3-24 05:18 作者: JAUNT 時間: 2025-3-24 07:13 作者: 啞巴 時間: 2025-3-24 11:03 作者: theta-waves 時間: 2025-3-24 16:33 作者: Bumptious 時間: 2025-3-24 21:37 作者: filial 時間: 2025-3-25 03:07 作者: Colonoscopy 時間: 2025-3-25 04:44
Deqiang Xiao,Chunfeng Lian,Li Wang,Hannah Deng,Kim-Han Thung,Pew-Thian Yap,James J. Xia,Dinggang Sheallenges to its successful implementation – ‘evidence-based’ teaching, teachers’ use of certain pedagogic strategies, the weak voice of children, ‘peer society’ and ‘a(chǎn)geism’ and the approach to social learning in the more abstract (theory-based) school subjects.作者: abysmal 時間: 2025-3-25 08:43 作者: 準(zhǔn)則 時間: 2025-3-25 15:27
allenges to its successful implementation – ‘evidence-based’ teaching, teachers’ use of certain pedagogic strategies, the weak voice of children, ‘peer society’ and ‘a(chǎn)geism’ and the approach to social learning in the more abstract (theory-based) school subjects.作者: 提升 時間: 2025-3-25 18:14 作者: contrast-medium 時間: 2025-3-25 22:31
Si Chen,Te-Ju Wu,Tai-Hsien Wu,Matthew Pastewait,Anna Zheng,Li Wang,Xiaoyu Wang,Ching-Chang Kot answers. My colleagues and I, however, thought that investigating human development directly could provide key insights into human congenital disease. The difficulty was that human embryonic tissues required for this research are intrinsically challenging to obtain: raising ethical, practical and 作者: 形容詞 時間: 2025-3-26 00:42 作者: Living-Will 時間: 2025-3-26 05:19 作者: mettlesome 時間: 2025-3-26 10:06 作者: Omnipotent 時間: 2025-3-26 15:12 作者: 有機(jī)體 時間: 2025-3-26 18:53 作者: Neutral-Spine 時間: 2025-3-27 00:01
Mary Lanier Zaytoun Berne,Feng-Chang Lin,Yi Li,Tai-Hsien Wu,Esther Chien,Ching-Chang Kond practice over the years.Examines pros and cons of the impThis book discusses key aspects of life in schools and classrooms, and surveys the changes that have occurred over the years in educational ?research, policy making and practice in these school and classroom settings. It not only examines c作者: ticlopidine 時間: 2025-3-27 03:44
Si Chen,Te-Ju Wu,Tai-Hsien Wu,Matthew Pastewait,Anna Zheng,Li Wang,Xiaoyu Wang,Ching-Chang Konvolved in its inception in 1999 and ultimately became the Resource’s co-Director for nearly 15?years. How did my scientific journey lead me to this position? I started my career as a research scientist in 1980, following the traditional pattern of Ph.D. then post-doctoral positions, initially study作者: 圓桶 時間: 2025-3-27 07:21
Machine Learning for CBCT Segmentation of Craniomaxillofacial Bony StructuresCone-beam computed tomography (CBCT) is routinely used to this end, by annotating the CMF bones (i.e., maxilla and mandible) from the CBCT volume. However, due to the poor quality of CBCT images, e.g., various image artifacts and very low signal-to-noise ratio, segmentation of CMF bones is a very ch作者: crumble 時間: 2025-3-27 10:15 作者: BAIL 時間: 2025-3-27 15:20
Segmenting Bones from Brain MRI via Generative Adversarial LearningUnfortunately. CT emits radiation and is not a safe imaging modality, especially for infant patients. Thus, there is a clinical need of using alternative safer modalities, e.g., magnetic resonance imaging (MRI), for those patient populations. Although MRI provides good image quality for soft tissue,作者: avenge 時間: 2025-3-27 21:03 作者: 倫理學(xué) 時間: 2025-3-27 23:35
Machine Learning for Facial Recognition in Orthodonticsagnosis and planning treatment in modern orthodontics. The observation of the cranio-facial morphology enables the detection of not only orthodontic or orthopedic problems (e.g., the size and positions of the maxilla and mandibles) but also genetic problems. Recent improvements in technologies with 作者: 怕失去錢 時間: 2025-3-28 04:21
Machine/Deep Learning for Performing Orthodontic Diagnoses and Treatment Planningto improve the plan quality, researchers have attempted to develop such systems. Artificial intelligence (AI) attempts to reflect advanced human intelligence in machines, and efforts to develop AI systems have been made since the advent of computers. In the 1980s, an expert system that expressed exp作者: Nonflammable 時間: 2025-3-28 06:59 作者: epidermis 時間: 2025-3-28 11:52
Machine (Deep) Learning for Characterization of Craniofacial Variations structure can reveal potential disorders that affect the patient’s quality of life. In recent years, the preferred method for diagnosis and treatment of patients with craniofacial disorders has been using Cone Beam Computed Tomography (CBCT) imaging accompanied by manual segmentation to produce a 3作者: PAEAN 時間: 2025-3-28 16:25
Patient-Specific Reference Model for Planning Orthognathic Surgeryuccess of craniomaxillofacial (CMF) surgery. An accurate surgical plan greatly relies on a patient-specific reference model. The current challenge is a lack of this reference model. As a result, the outcome of surgery is currently dependent on the surgeon’s diagnoses and experience. This chapter int作者: 縮影 時間: 2025-3-28 20:55 作者: 高爾夫 時間: 2025-3-28 23:03 作者: 牌帶來 時間: 2025-3-29 07:02 作者: nepotism 時間: 2025-3-29 09:56
Machine Learning and Deep Learning in Genetics and Genomicsbegin with a general introduction of genomics data and present a multi-omics study investigating early childhood oral health. We then review statistical methods and ML/DL methods and their application in genomics data analysis that include the following aspects: (1) association between genetic marke作者: FLORA 時間: 2025-3-29 14:40
Machine (Deep) Learning and Finite Element Modelingovement, implant components, and peri-implant bone. We begin with a brief introduction of the traditional FEA process and disadvantages of using FEA in clinical applications. Then, we review existing studies in which researchers use machine learning (ML) to address these disadvantages. Finally, we c作者: 采納 時間: 2025-3-29 16:04
978-3-030-71883-1Springer Nature Switzerland AG 2021作者: 信條 時間: 2025-3-29 20:05
Ching-Chang Ko,Dinggang Shen,Li WangReviews use of machine learning in contemporary dentistry.Covers applications in dental practice and research.Highlights benefits, opportunities, and challenges作者: tolerance 時間: 2025-3-30 03:56 作者: 卜聞 時間: 2025-3-30 06:59
https://doi.org/10.1007/978-3-030-71881-7Dental big data; Digital oral imaging; Digital Dentistry; Oral diagnosis; Artificial intelligence in den作者: Accommodation 時間: 2025-3-30 10:57
Assessment of Outcomes by Using Machine Learnings chapter, we provide an overview of different machine learning approaches used to analyze large datasets and the different applications of machine learning tools in craniofacial genomics, imaging, and remoted dental monitoring. Machine learning has shown much promise as we move forward in the era of personalized medicine/dentistry.作者: Perennial長期的 時間: 2025-3-30 13:37 作者: Melatonin 時間: 2025-3-30 19:20
Book 2021iagnosis and treatment, dental designs, and dental research. Machine learning is an emerging field of artificial intelligence research and practice in which computer agents are employed to improve perception, cognition, and action based on their ability to “l(fā)earn”, for example through use of big dat作者: 苦澀 時間: 2025-3-30 23:47 作者: coltish 時間: 2025-3-31 01:19
