標(biāo)題: Titlebook: Data Driven Approaches on Medical Imaging; Bin Zheng,Stefan Andrei,Kishor Datta Gupta Book 2023 The Editor(s) (if applicable) and The Auth [打印本頁] 作者: malfeasance 時間: 2025-3-21 18:19
書目名稱Data Driven Approaches on Medical Imaging影響因子(影響力)
書目名稱Data Driven Approaches on Medical Imaging影響因子(影響力)學(xué)科排名
書目名稱Data Driven Approaches on Medical Imaging網(wǎng)絡(luò)公開度
書目名稱Data Driven Approaches on Medical Imaging網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Data Driven Approaches on Medical Imaging被引頻次
書目名稱Data Driven Approaches on Medical Imaging被引頻次學(xué)科排名
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書目名稱Data Driven Approaches on Medical Imaging年度引用學(xué)科排名
書目名稱Data Driven Approaches on Medical Imaging讀者反饋
書目名稱Data Driven Approaches on Medical Imaging讀者反饋學(xué)科排名
作者: dura-mater 時間: 2025-3-21 22:38 作者: 細(xì)節(jié) 時間: 2025-3-22 02:20 作者: 富饒 時間: 2025-3-22 08:07 作者: 比喻好 時間: 2025-3-22 10:26
Automl Systems for Medical Imaging, human expertise and computerized systems can result in improved diagnostic accuracy. An automated machine learning approach simplifies the creation of custom image recognition models by utilizing neural architecture search and transfer learning techniques. Medical imaging techniques are used to non作者: packet 時間: 2025-3-22 15:42
Online Learning for X-Ray, CT or MRI,maging (MRI) are a few examples of medical imaging. Most of the time, these imaging techniques are utilized to examine and diagnose diseases. Medical professionals identify the problem after analyzing the images. However, manual identification can be challenging because the human eye is not always a作者: packet 時間: 2025-3-22 20:10
Invariant Scattering Transform for Medical Imaging,mputation using Convolutional Neural Networks (CNN) to capture patterns’ scale and orientation in the input signal. IST aims to be invariant to transformations that are common in medical images, such as translation, rotation, scaling, and deformation, used to improve the performance in medical imagi作者: 變態(tài) 時間: 2025-3-22 23:01
Generative Adversarial Networks for Data Augmentation,ata augmentation. GANs work by employing a generator network to create new data samples that are then assessed by a discriminator network to determine their similarity to real samples. The discriminator network is taught to differentiate between actual and synthetic samples, while the generator syst作者: Prologue 時間: 2025-3-23 04:17 作者: 冰雹 時間: 2025-3-23 08:09 作者: 鋼盔 時間: 2025-3-23 13:05 作者: 遭遇 時間: 2025-3-23 14:54 作者: START 時間: 2025-3-23 18:07
Bin Zheng,Stefan Andrei,Kishor Datta Guptaliterature review and theoretical concept of computer vision, and challenges in medical imaging.a description of current technologies about different medical imaging system.highlight challenges in dev作者: 充滿人 時間: 2025-3-24 01:43
http://image.papertrans.cn/d/image/262777.jpg作者: Melanoma 時間: 2025-3-24 04:47
https://doi.org/10.1007/978-3-031-34579-1-ray, Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Nuclear Imaging, Ultrasound, Electrical Impedance Tomography (EIT), and Emerging Technologies for in vivo imaging are widely used in today’s healthcare throughout the world. A review of medical imaging modalities is presented in this 作者: Pelago 時間: 2025-3-24 07:26 作者: Gratuitous 時間: 2025-3-24 13:53
https://doi.org/10.1007/978-3-031-07357-1 medical imaging field can significantly improve in terms of the speed and accuracy of the diagnostic process. Computed tomography (CT), magnetic resonance imaging (MRI), X-ray imaging, ultrasound imaging, and positron emission tomography (PET) are the most commonly used types of imaging data in the作者: 窩轉(zhuǎn)脊椎動物 時間: 2025-3-24 15:34
https://doi.org/10.1007/978-3-031-07357-1es from unstructured data. Though this flourishing context is developing in the medical image processing sector, scarcity of problem-dependent training data has become a larger issue in the way of easy application of deep learning in the medical sector. To unravel the confined data source, researche作者: nitroglycerin 時間: 2025-3-24 20:34 作者: nitroglycerin 時間: 2025-3-25 00:34 作者: aesthetician 時間: 2025-3-25 07:07 作者: 土坯 時間: 2025-3-25 09:48
https://doi.org/10.1007/978-3-031-07357-1ata augmentation. GANs work by employing a generator network to create new data samples that are then assessed by a discriminator network to determine their similarity to real samples. The discriminator network is taught to differentiate between actual and synthetic samples, while the generator syst作者: 冒失 時間: 2025-3-25 15:20
https://doi.org/10.1007/978-3-031-07357-1ment of diseases. However, there are several ethical concerns associated with medical imaging research, including bias and explainability of the decision-making process. In this chapter, we will discuss these concerns and how they can be addressed in medical imaging research. Bias refers to the syst作者: 颶風(fēng) 時間: 2025-3-25 19:42
https://doi.org/10.1007/978-3-031-07357-1ssues and organs of the body. These images provide vital information for clinical diagnosis, and in this chapter, we will explore the use of X-ray, MRI, and nuclear imaging in detecting severe illnesses. However, manual evaluation and storage of these images can be a challenging and time-consuming p作者: Visual-Acuity 時間: 2025-3-25 23:51
different medical imaging system.highlight challenges in dev.This book deals with the recent advancements in computer vision techniques such as active learning, few-shot learning, zero shot learning, explainable and interpretable ML, online learning, AutoML etc. and their applications in medical dom作者: 地名詞典 時間: 2025-3-26 02:42
https://doi.org/10.1007/978-3-031-07357-1e used to develop new quantitative image markers and prediction models for disease detection, diagnosis, and prognosis prediction. By covering the basic knowledge of medical imaging informatics, this chapter provides a foundation for understanding the role of informatics in medicine and its potential impact on patient care.作者: 反復(fù)無常 時間: 2025-3-26 06:31
https://doi.org/10.1007/978-3-031-07357-1-invasively create images of internal organs and body parts for diagnostic and procedural purposes. This article aims to highlight the potential applications, strategies, and techniques of AutoML in medical imaging through theoretical and empirical evidence.作者: Truculent 時間: 2025-3-26 12:07
https://doi.org/10.1007/978-3-031-07357-1ce medical image analysis outcomes. This study provides an overview of IST in medical imaging by considering the types of IST, their application, limitations, and potential scopes for future researchers and practitioners.作者: 顛簸地移動 時間: 2025-3-26 13:51
Invariant Scattering Transform for Medical Imaging,ce medical image analysis outcomes. This study provides an overview of IST in medical imaging by considering the types of IST, their application, limitations, and potential scopes for future researchers and practitioners.作者: Antigen 時間: 2025-3-26 17:21 作者: 燦爛 時間: 2025-3-26 21:06
Introduction to Medical Imaging Informatics,e used to develop new quantitative image markers and prediction models for disease detection, diagnosis, and prognosis prediction. By covering the basic knowledge of medical imaging informatics, this chapter provides a foundation for understanding the role of informatics in medicine and its potential impact on patient care.作者: 哎呦 時間: 2025-3-27 03:11 作者: NADIR 時間: 2025-3-27 07:33 作者: 遵循的規(guī)范 時間: 2025-3-27 13:02 作者: FLACK 時間: 2025-3-27 14:53
Few Shot Learning for Medical Imaging: A Comparative Analysis of Methodologies and Formal Mathematimelight in this data scarcity sector. In this chapter, the background, and basic overview of a few shots of learning are represented. Henceforth, the classification of few shot learning is described also. Even, the paper shows a comparison of methodological approaches that are applied in medical ima作者: Eeg332 時間: 2025-3-27 19:59 作者: craven 時間: 2025-3-27 22:18 作者: Expressly 時間: 2025-3-28 03:53 作者: Microaneurysm 時間: 2025-3-28 09:55
Case Studies on X-ray Imaging, MRI and Nuclear Imaging,medical imaging technology. CNN is a commonly used approach for image analysis due to its ability to extract features from raw input images, and as such, will be the primary area of discussion in this study. Therefore, we have considered CNN as our discussion area in this study to diagnose ailments 作者: Substance-Abuse 時間: 2025-3-28 12:50 作者: 紋章 時間: 2025-3-28 16:52 作者: harangue 時間: 2025-3-28 18:57 作者: vitreous-humor 時間: 2025-3-29 00:08 作者: 突襲 時間: 2025-3-29 03:41 作者: Prostaglandins 時間: 2025-3-29 10:33
https://doi.org/10.1007/978-3-031-07357-1alysis. Medical research is already entered a new era of research which is called Artificial Intelligence (AI). AI can automatically find complex patterns from an image and identify diseases. Methods for medical imaging that uses AI techniques will be covered in this chapter.作者: 公式 時間: 2025-3-29 14:56
