Titlebook: Deep Learning for Biomedical Data Analysis; Techniques, Approach Mourad Elloumi Book 2021 Springer Nature Switzerland AG 2021 Deep Learning

OAK

書目名稱Deep Learning for Biomedical Data Analysis影響因子(影響力)




書目名稱Deep Learning for Biomedical Data Analysis影響因子(影響力)學(xué)科排名




書目名稱Deep Learning for Biomedical Data Analysis網(wǎng)絡(luò)公開度




書目名稱Deep Learning for Biomedical Data Analysis網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Deep Learning for Biomedical Data Analysis被引頻次




書目名稱Deep Learning for Biomedical Data Analysis被引頻次學(xué)科排名




書目名稱Deep Learning for Biomedical Data Analysis年度引用




書目名稱Deep Learning for Biomedical Data Analysis年度引用學(xué)科排名




書目名稱Deep Learning for Biomedical Data Analysis讀者反饋




書目名稱Deep Learning for Biomedical Data Analysis讀者反饋學(xué)科排名

Dorsal

amygdala

Infuriate

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

取之不竭

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

填料

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

填料

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

經(jīng)典

Corroborate

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

Lignans