Titlebook: Handbook of Artificial Intelligence in Healthcare; Vol. 1 - Advances an Chee-Peng Lim,Ashlesha Vaidya,Lakhmi C. Jain Book 2022 The Editor(s

Wilson

書目名稱Handbook of Artificial Intelligence in Healthcare影響因子(影響力)




書目名稱Handbook of Artificial Intelligence in Healthcare影響因子(影響力)學(xué)科排名




書目名稱Handbook of Artificial Intelligence in Healthcare網(wǎng)絡(luò)公開度




書目名稱Handbook of Artificial Intelligence in Healthcare網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Handbook of Artificial Intelligence in Healthcare被引頻次




書目名稱Handbook of Artificial Intelligence in Healthcare被引頻次學(xué)科排名




書目名稱Handbook of Artificial Intelligence in Healthcare年度引用




書目名稱Handbook of Artificial Intelligence in Healthcare年度引用學(xué)科排名




書目名稱Handbook of Artificial Intelligence in Healthcare讀者反饋




書目名稱Handbook of Artificial Intelligence in Healthcare讀者反饋學(xué)科排名

處理

萬花筒

https://doi.org/10.1007/978-981-13-1177-2rders that make it difficult the communication with other people. Their natural language presents different degrees of alteration, reaching in some cases the impossibility of speaking. This chapter presents an approach to model the patient’s behavior by processing recordings during the therapy. Vide

過于光澤

https://doi.org/10.1007/978-3-031-61191-9sis is as evident in medicine as it is elsewhere. Numerous artificial intelligence techniques been applied to different medical problems with the aim of automating time-consuming, and often subjective, manual tasks implemented by practitioners in diverse specialties. This chapter focuses on several

Annotate

ADJ

The Audience as Myth and Realitynical background and setting for the opportunistic development of cancer imaging biomarkers from such routine imaging. The chapter is aimed at the clinicians with a data science interest as well as data scientists with a clinical interest, and touches on computational approaches on radiological data

Gossamer

https://doi.org/10.1057/9781137478818el of multiple input and multiple output structure to detect LST-type polyp with high accuracy, which is based on U-Net architecture for the segmentation. Not only the original endoscope image but also depth map is also used to the original CNN structure of 2 inputs and 4 outputs. Here, proposed met

Agility

令人作嘔

https://doi.org/10.1007/978-1-349-20540-0g used to detect the presence of subclinical atherosclerosis since it provides a measurement of the Intima Media Thickness (IMT) of the artery and can be used to identify the presence of atherosclerotic plaques. Moreover, it is well known that disruption of an atherosclerotic plaque plays a crucial

Phenothiazines

https://doi.org/10.1057/9780230276499 a major cause of early postoperative recurrence of HCC. Predicting MVI before surgery can help doctors develop treatment plans. However, the diagnosis of MVI depends on postoperative pathological verification, which is difficult to predict before surgery. In recent years, more researchers have used