標(biāo)題: Titlebook: Artificial Intelligence and Machine Learning for Healthcare; Vol. 1: Image and Da Chee-Peng Lim,Ashlesha Vaidya,Lakhmi C. Jain Book 2023 Th [打印本頁] 作者: 無限 時(shí)間: 2025-3-21 20:01
書目名稱Artificial Intelligence and Machine Learning for Healthcare影響因子(影響力)
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書目名稱Artificial Intelligence and Machine Learning for Healthcare網(wǎng)絡(luò)公開度學(xué)科排名
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書目名稱Artificial Intelligence and Machine Learning for Healthcare讀者反饋
書目名稱Artificial Intelligence and Machine Learning for Healthcare讀者反饋學(xué)科排名
作者: 粗魯性質(zhì) 時(shí)間: 2025-3-21 20:58 作者: Genetics 時(shí)間: 2025-3-22 02:31 作者: Expurgate 時(shí)間: 2025-3-22 08:34
1868-4394 ing populations using the recently evolved artificial intell.Artificial intelligence (AI) and machine learning (ML) have transformed many standard and conventional methods in undertaking health and well-being issues of humans.? AL/ML-based systems and tools play a critical role in this digital and b作者: PALSY 時(shí)間: 2025-3-22 11:10 作者: 歪曲道理 時(shí)間: 2025-3-22 16:19
Radiomics: Approach to Precision Medicine,c in recent years. Large-scale molecular-biology-level information, such as genome, proteome, and metabolome, is collected from patients for analyzing biomarkers for subpopulation of a particular disease. This is import for the targeted therapy, which is expected to be more effective and less harmfu作者: Pandemic 時(shí)間: 2025-3-22 18:19 作者: 清楚說話 時(shí)間: 2025-3-22 22:55
,Unsupervised Domain Adaptation Approach for?Liver Tumor Detection in?Multi-phase CT Images,ely used in various medical applications. Deep learning-based AI systems require a large amount of training data for model learning. However, acquiring sufficient training data with high-quality annotation is a major challenge in Healthcare. As a result, deep learning-based models face a lack of ann作者: 絕種 時(shí)間: 2025-3-23 01:56 作者: liposuction 時(shí)間: 2025-3-23 05:36 作者: inspired 時(shí)間: 2025-3-23 11:29
Polyp Segmentation with Deep Ensembles and Data Augmentation,l rate of this cancer, but this intervention depends on the accurate detection of polys in the surrounding tissues. Missing a poly has serious consequences. One way to guard against human error is to develop automatic polyp detection systems. Deep learning semantic segmentation offers one approach t作者: alabaster 時(shí)間: 2025-3-23 16:45
Autistic Verbal Behavior Parameters,expressions are hard to understand for them. Biotech is a research project to get new alternates to help these individuals to communicate. The main goal here is to provide advances and tuned tools through audio and video real-time processing. Part of the previous work in this research stated the bas作者: RADE 時(shí)間: 2025-3-23 18:50
Advances in Modelling Hospital Medical Wards,ts with larger clinical complexity and needs. The patients in medical wards exhibit multiple pathologies, with a burden of activities, risks, and costs for health systems, mining their sustainability. Internal Medicine Departments play an important role in the care of those patients that access the 作者: ear-canal 時(shí)間: 2025-3-24 00:55 作者: 平淡而無味 時(shí)間: 2025-3-24 02:31
BioGNN: How Graph Neural Networks Can Solve Biological Problems,ain feature is the capability of processing graph structured data with minimal loss of structural information. This makes GNNs the ideal family of models for processing a wide variety of biological data: metabolic networks, structural formulas of molecules, and proteins are all examples of biologica作者: 巡回 時(shí)間: 2025-3-24 07:31 作者: 小故事 時(shí)間: 2025-3-24 13:43
eCustomer Relationship Management,iction of outcome of patients, etc. Genetic tests can provide prognostic information in breast cancer for both diagnosis and treatment planning. In this study, we developed a radiogenomics method to discover imaging biomarkers on breast MRI for prediction of genetic test results for breast cancer by作者: 異教徒 時(shí)間: 2025-3-24 14:57
https://doi.org/10.1007/978-3-540-85017-5he process. As an example of the effective synergy between AI and data-driven acquisition/reconstruction in radial MRI, we present a GReedy Adaptive Data-driven Environment (GRADE) for intelligent radial sampling that uses the power spectrum of the reconstructed image and AI-based superresolution st作者: irradicable 時(shí)間: 2025-3-24 20:27
https://doi.org/10.1007/978-3-540-85017-5ve proposed domain adaptation-based technique for liver tumor detection in multi-phase CT images. We discuss the domain-shift problem in different phases of multiphase liver CT images and introduce our domain adaptation technique for multi-phase CT images. We have used PV phase images to learn a mod作者: Feedback 時(shí)間: 2025-3-25 00:35
eCustomer Relationship Management,eal datasets during network training. The main characteristic of our method, differently from other existing techniques, lies in the generation procedure carried out in multiple steps, based on the intuition that, by splitting the procedure in multiple phases, the overall generation task is simplifi作者: JECT 時(shí)間: 2025-3-25 03:42 作者: foppish 時(shí)間: 2025-3-25 10:21 作者: 痛恨 時(shí)間: 2025-3-25 14:12
eCustomer Relationship Management,of head bouncing and can be extended to some other movements during interactions recorded in diverse circumstances with ASD patients. A short tracking and lightweight processing approach is presented, and applied to a small video test set. Results are validated against manually detected movements. A作者: output 時(shí)間: 2025-3-25 19:02 作者: Intellectual 時(shí)間: 2025-3-25 21:58
eCustomer Relationship Management,s. For instance, drug side-effects were predicted based on a graph describing the interactions between drugs and human genes. Another very important innovation was brought by generative models, that were introduced for graph data after the success of generative models for images. In particular, GNNs作者: CHOKE 時(shí)間: 2025-3-26 02:25 作者: 靈敏 時(shí)間: 2025-3-26 06:44
Artificial Intelligence and Machine Learning for HealthcareVol. 1: Image and Da作者: 發(fā)微光 時(shí)間: 2025-3-26 09:48 作者: 全國性 時(shí)間: 2025-3-26 15:46 作者: gruelling 時(shí)間: 2025-3-26 18:53 作者: AUGUR 時(shí)間: 2025-3-26 22:22 作者: CANDY 時(shí)間: 2025-3-27 04:34
Classification of Arrhythmia Signals Using Hybrid Convolutional Neural Network (CNN) Model, approach based on long short-term memory (LSTM) approach. Experimental data are obtained from PhysioNet CinC Challenge 2017 database. ECG signals are preprocessed via filtering, QRS detection, segmentation and median wave selection. One-dimensional CNN, hybrid CNN–long short-term memory (CNN–LSTM) 作者: Efflorescent 時(shí)間: 2025-3-27 05:37
Polyp Segmentation with Deep Ensembles and Data Augmentation,een loss functions. Our best ensembles are tested on a large dataset composed of samples taken from five polyp benchmarks. Ensembles are assessed and compared with the best method reported in the literature and shown to produce state-of-the-art results. The source code, the dataset, and the testing 作者: macrophage 時(shí)間: 2025-3-27 10:01
Autistic Verbal Behavior Parameters,of head bouncing and can be extended to some other movements during interactions recorded in diverse circumstances with ASD patients. A short tracking and lightweight processing approach is presented, and applied to a small video test set. Results are validated against manually detected movements. A作者: excrete 時(shí)間: 2025-3-27 15:51 作者: 無所不知 時(shí)間: 2025-3-27 20:42 作者: agitate 時(shí)間: 2025-3-27 23:53
https://doi.org/10.1007/978-3-031-11154-9Artificial Intelligence; Deep Learning; Intelligent Technology; Assistive Technology; Ageing Populations作者: Lacunar-Stroke 時(shí)間: 2025-3-28 06:06 作者: 笨重 時(shí)間: 2025-3-28 06:45
eCustomer Relationship Management,wn and commonly used AI algorithms are presented. We continued the chapter with the pros and cons of AI, highlighting the main advantages and disadvantages it has. The chapter finishes with some of the newest real-life applications of AI in different healthcare sectors from diagnostic, drug developm作者: Loathe 時(shí)間: 2025-3-28 11:05 作者: AVOW 時(shí)間: 2025-3-28 15:06 作者: 似少年 時(shí)間: 2025-3-28 19:51 作者: 大都市 時(shí)間: 2025-3-29 00:55 作者: OPINE 時(shí)間: 2025-3-29 05:07
https://doi.org/10.1007/978-3-540-85017-5 Atrial fibrillation (AF) is a common type of arrhythmia that can be diagnosed using an electrocardiogram (ECG) pattern. Identification of arrhythmia through ECG can be very challenging because the process is highly dependent on experts and very time consuming. The use of deep learning in automatica作者: Etymology 時(shí)間: 2025-3-29 10:13
eCustomer Relationship Management,l rate of this cancer, but this intervention depends on the accurate detection of polys in the surrounding tissues. Missing a poly has serious consequences. One way to guard against human error is to develop automatic polyp detection systems. Deep learning semantic segmentation offers one approach t作者: hemoglobin 時(shí)間: 2025-3-29 11:25 作者: FLAIL 時(shí)間: 2025-3-29 17:05
eCustomer Relationship Management,ts with larger clinical complexity and needs. The patients in medical wards exhibit multiple pathologies, with a burden of activities, risks, and costs for health systems, mining their sustainability. Internal Medicine Departments play an important role in the care of those patients that access the 作者: Herbivorous 時(shí)間: 2025-3-29 20:48
eCustomer Relationship Management,e other success measures in hearing rehabilitation. A Living Lab approach was applied. Living Lab projects at their heart apply co-design in real-life contexts involving multiple stakeholders to achieve innovative products and services for the people involved—in this project, patients as well as sta作者: cruise 時(shí)間: 2025-3-30 00:22
eCustomer Relationship Management,ain feature is the capability of processing graph structured data with minimal loss of structural information. This makes GNNs the ideal family of models for processing a wide variety of biological data: metabolic networks, structural formulas of molecules, and proteins are all examples of biologica作者: cancellous-bone 時(shí)間: 2025-3-30 06:27
Artificial Intelligence and Machine Learning for Healthcare978-3-031-11154-9Series ISSN 1868-4394 Series E-ISSN 1868-4408 作者: Archipelago 時(shí)間: 2025-3-30 10:14
