標(biāo)題: Titlebook: Deep Learning for Biomedical Data Analysis; Techniques, Approach Mourad Elloumi Book 2021 Springer Nature Switzerland AG 2021 Deep Learning [打印本頁(yè)] 作者: OAK 時(shí)間: 2025-3-21 18:58
書(shū)目名稱Deep Learning for Biomedical Data Analysis影響因子(影響力)
書(shū)目名稱Deep Learning for Biomedical Data Analysis影響因子(影響力)學(xué)科排名
書(shū)目名稱Deep Learning for Biomedical Data Analysis網(wǎng)絡(luò)公開(kāi)度
書(shū)目名稱Deep Learning for Biomedical Data Analysis網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱Deep Learning for Biomedical Data Analysis被引頻次
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書(shū)目名稱Deep Learning for Biomedical Data Analysis年度引用學(xué)科排名
書(shū)目名稱Deep Learning for Biomedical Data Analysis讀者反饋
書(shū)目名稱Deep Learning for Biomedical Data Analysis讀者反饋學(xué)科排名
作者: Dorsal 時(shí)間: 2025-3-21 23:57 作者: amygdala 時(shí)間: 2025-3-22 03:19 作者: Infuriate 時(shí)間: 2025-3-22 05:30
Designing Maintainable Softwaremedical imaging has emerged as a grand success for medical diagnostics and analysis. So, medical image fusion has become a suitable subsidiary for medical experts. This chapter aims to focus on different aspects of image fusion techniques and their applications in the area of medical imaging. This c作者: 取之不竭 時(shí)間: 2025-3-22 11:07
Designing Maintainable Softwarelysis of a much larger set of variables combined with sophisticated imaging and analytic techniques, the traditional paradigm of pathology based on visually descriptive microscopy can be complemented and substantially improved by digital pathology, utilizing screen-based visualization of digital tis作者: 填料 時(shí)間: 2025-3-22 15:32
https://doi.org/10.1007/978-3-031-34214-1to be a different disease. That means the genomic activities varies among these different diseases and the normal tissue as well. Thanks to the power of computing, . (DL) techniques have become feasible to integrate multi-omics data generated from the cells/tissue to study the outcomes of cancer as 作者: 填料 時(shí)間: 2025-3-22 17:50
Rafael F?o de Moura,Luigi Carrommon diseases, the amount of available labeled data is often insufficient, and a variety of strategies are being explored to deal with inadequate, noisy and missing data. This chapter describes the benefits of using DL models with EMR data for research to improve provisioning of health care in prima作者: 經(jīng)典 時(shí)間: 2025-3-23 00:17 作者: Corroborate 時(shí)間: 2025-3-23 02:39
Classification of Sequences with Deep Artificial Neural Networks: Representation and Architectural Ifficacy of (1) different DNA sequence representations and (2) several . (DL) architectures that process sequences for the solution of the related supervised classification problems. Although developed for specific classification tasks, we think that such architectures could be served as a suggestion作者: Lignans 時(shí)間: 2025-3-23 09:15 作者: Affable 時(shí)間: 2025-3-23 10:06
Medical Image Retrieval System Using Deep Learning Techniquesave discussed the different hand-crafted image features based retrieval systems to understand the perspectives of this research field. Here, we aim to congregate the weaknesses and constraints of the conventional retrieval systems and respective solutions with the help of the advanced DL algorithms.作者: Inelasticity 時(shí)間: 2025-3-23 14:09 作者: APO 時(shí)間: 2025-3-23 19:57 作者: 上流社會(huì) 時(shí)間: 2025-3-23 23:21
Deep Learning in Multi-Omics Data Integration in Cancer Diagnosticto be a different disease. That means the genomic activities varies among these different diseases and the normal tissue as well. Thanks to the power of computing, . (DL) techniques have become feasible to integrate multi-omics data generated from the cells/tissue to study the outcomes of cancer as 作者: 口味 時(shí)間: 2025-3-24 04:30
Using Deep Learning with Canadian Primary Care Data for Disease Diagnosismmon diseases, the amount of available labeled data is often insufficient, and a variety of strategies are being explored to deal with inadequate, noisy and missing data. This chapter describes the benefits of using DL models with EMR data for research to improve provisioning of health care in prima作者: Facilities 時(shí)間: 2025-3-24 08:45 作者: FIN 時(shí)間: 2025-3-24 12:53
Book 2021ications of the presented techniques and approaches on other topics in biomedical data analysis. The book finds a balance between theoretical and practical coverage of a wide range of issues in the field of biomedical data analysis, thanks to DL. The few published books on DL for biomedical data ana作者: narcotic 時(shí)間: 2025-3-24 16:55 作者: 不朽中國(guó) 時(shí)間: 2025-3-24 21:14
Book 2021, with both a broad coverage and enough depth to be of practical use to working professionals. This book offers enough fundamental and technical information on these techniques, approaches and the related problems without overcrowding the reader‘s head. It presents the results of the latest investig作者: seduce 時(shí)間: 2025-3-25 02:17
IoT Applications in Health Care,h dimensionality of microarray data and different deep learning classification techniques such as 2-. (2D- CNN) and 1-. CNN (1D-CNN). The proposed method used the fisher criterion and 1D-CNN techniques for microarray cancer samples prediction.作者: 進(jìn)步 時(shí)間: 2025-3-25 04:32
Designing Maintainable Softwareled data. An encoder, part of a . (CVAE), is used as a data projection for a 2D-visualization. The input vectors are encoded into a 2D-latent space, which helps the expert to visually analyze the spatial distribution of the training data set.作者: Capture 時(shí)間: 2025-3-25 10:24
1-Dimensional Convolution Neural Network Classification Technique for Gene Expression Datah dimensionality of microarray data and different deep learning classification techniques such as 2-. (2D- CNN) and 1-. CNN (1D-CNN). The proposed method used the fisher criterion and 1D-CNN techniques for microarray cancer samples prediction.作者: 藐視 時(shí)間: 2025-3-25 14:54
Innovative Deep Learning Approach for Biomedical Data Instantiation and Visualizationled data. An encoder, part of a . (CVAE), is used as a data projection for a 2D-visualization. The input vectors are encoded into a 2D-latent space, which helps the expert to visually analyze the spatial distribution of the training data set.作者: inconceivable 時(shí)間: 2025-3-25 16:40 作者: 聽(tīng)寫(xiě) 時(shí)間: 2025-3-25 21:52
echnical information on Deep Learning techniques, approachesThis book is the first overview on Deep Learning (DL) for biomedical data analysis. It surveys the most recent techniques and approaches in this field, with both a broad coverage and enough depth to be of practical use to working profession作者: seduce 時(shí)間: 2025-3-26 03:00
Designing Maintainable Softwareether. Different advanced optical imaging methods, whether invasive or non-invasive, are applicable to a wide variety of biomedical research, and CNN algorithms can be tailored to assist with extracting meaningful results from imaging data.作者: adipose-tissue 時(shí)間: 2025-3-26 04:39 作者: 高歌 時(shí)間: 2025-3-26 10:00
Deep Learning for Lung Disease Detection from Chest X-Rays ImagesDL techniques used to detect lung diseases from chest x-rays datasets. It contains the description of the public datasets of chest x-rays images available for thoracic disease detection, tuberculosis screening and lung nodule detection. It also lists most commonly used performance metrics for the evaluation of disease detection techniques.作者: Temporal-Lobe 時(shí)間: 2025-3-26 14:36 作者: 令人不快 時(shí)間: 2025-3-26 17:55
http://image.papertrans.cn/d/image/264601.jpg作者: 無(wú)底 時(shí)間: 2025-3-26 21:45 作者: Dorsal-Kyphosis 時(shí)間: 2025-3-27 03:46 作者: CHAR 時(shí)間: 2025-3-27 06:54
Nobukazu Nakagoshi,Jhonamie A. Mabuhaynscriptional level and cause translational inhibition or mRNA cleavage. Quick and effective detection of the binding sites of miRNAs is a major problem in bioinformatics. This chapter introduces a new technique to model microRNA-target binding using . (RNN) over a miRNA-target duplex sequence repres作者: disparage 時(shí)間: 2025-3-27 13:27 作者: delegate 時(shí)間: 2025-3-27 16:00
Designing Maintainable Softwareta-set effectively and efficiently, as per the users requirements. Nowadays, the immense advancements in the field of Digital Imaging have exponentially increased the real-time applications of the CBIR techniques. Researchers around the globe are using different CBIR techniques in the field of educa作者: 我要沮喪 時(shí)間: 2025-3-27 20:20 作者: 整頓 時(shí)間: 2025-3-27 23:20
Designing Maintainable Softwareovements of light microscopy enabled wide-spread use of structural criteria to define diseases. Since then, the quality of optical instruments has been constantly evolving. However the central element of the diagnostic process remains the knowledge and experience of pathologists visually classifying作者: Offensive 時(shí)間: 2025-3-28 03:42
Designing Maintainable Softwaressifying massive database by human expert is mostly unfeasible, being—in certain limited conditions (still, extremely time-consuming)—partially been done, only for simple signatures, easily recognizable by an expert. Concerning this aspect, medical experts face two challenging problems: how to selec作者: 紅潤(rùn) 時(shí)間: 2025-3-28 08:37
Designing Maintainable Software tractable goals and problem solving approaches, it is crucial to understand how both imaging and computational tools have been developed and used together. Different advanced optical imaging methods, whether invasive or non-invasive, are applicable to a wide variety of biomedical research, and CNN 作者: syncope 時(shí)間: 2025-3-28 12:07
Designing Maintainable Softwarex-rays images. In the past decade, the concept of automatic disease detection from the datasets of chest x-rays images has gained importance and researchers have proposed a variety of techniques for tuberculosis screening, thoracic disease detection and lung nodule detection. With the availability o作者: 眼界 時(shí)間: 2025-3-28 16:37 作者: 石墨 時(shí)間: 2025-3-28 20:37 作者: neologism 時(shí)間: 2025-3-29 00:01 作者: SKIFF 時(shí)間: 2025-3-29 03:09
https://doi.org/10.1007/978-3-030-71676-9Deep Learning (DL); Biomedical Data Analysis; Biomedical Image Analysis; Medical Diagnostics; Artificial作者: Meander 時(shí)間: 2025-3-29 09:20 作者: Gene408 時(shí)間: 2025-3-29 14:10 作者: creatine-kinase 時(shí)間: 2025-3-29 15:44
Nobukazu Nakagoshi,Jhonamie A. Mabuhaynscriptional level and cause translational inhibition or mRNA cleavage. Quick and effective detection of the binding sites of miRNAs is a major problem in bioinformatics. This chapter introduces a new technique to model microRNA-target binding using . (RNN) over a miRNA-target duplex sequence representation.作者: 充滿人 時(shí)間: 2025-3-29 23:42
1-Dimensional Convolution Neural Network Classification Technique for Gene Expression Datadata, which has a large number of features. DNA microarray technology is an approach to monitor the expression levels of sizable genes simultaneously. Microarray gene expression data is more useful for predicting and understanding various diseases such as cancer. Most of the microarray data are beli作者: 裙帶關(guān)系 時(shí)間: 2025-3-30 02:13
Classification of Sequences with Deep Artificial Neural Networks: Representation and Architectural Ialysis is represented by sequence classification, a methodology that is widely used to analyze sequential data of different nature. However, its application to DNA sequences requires a proper representation of such sequences, which is still an open research problem. . (ML) methodologies have given a作者: Oratory 時(shí)間: 2025-3-30 06:48 作者: altruism 時(shí)間: 2025-3-30 09:02 作者: 悄悄移動(dòng) 時(shí)間: 2025-3-30 16:04 作者: 愚笨 時(shí)間: 2025-3-30 18:51 作者: 繁殖 時(shí)間: 2025-3-30 23:11 作者: 諷刺 時(shí)間: 2025-3-31 03:29 作者: Frequency 時(shí)間: 2025-3-31 05:25
Convolutional Neural Networks in Advanced Biomedical Imaging Applications tractable goals and problem solving approaches, it is crucial to understand how both imaging and computational tools have been developed and used together. Different advanced optical imaging methods, whether invasive or non-invasive, are applicable to a wide variety of biomedical research, and CNN
