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Titlebook: Radiomics and Radiogenomics in Neuro-oncology; First International Hassan Mohy-ud-Din,Saima Rathore Conference proceedings 2020 Springer N

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發(fā)表于 2025-3-21 18:59:19 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Radiomics and Radiogenomics in Neuro-oncology
副標題First International
編輯Hassan Mohy-ud-Din,Saima Rathore
視頻videohttp://file.papertrans.cn/821/820870/820870.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Radiomics and Radiogenomics in Neuro-oncology; First International  Hassan Mohy-ud-Din,Saima Rathore Conference proceedings 2020 Springer N
描述.This book constitutes the proceedings of the First International Workshop on Radiomics and Radiogenomics in Neuro-oncology, RNO-AI 2019, which was held in conjunction with MICCAI in Shenzhen, China, in October 2019. ..The 10 full papers presented in this volume were carefully reviewed and selected from 15 submissions. They deal with the development of tools that can automate the analysis and synthesis of neuro-oncologic imaging.?.
出版日期Conference proceedings 2020
關鍵詞artificial intelligence; bioinformatics; classification; classification methods; computer vision; correla
版次1
doihttps://doi.org/10.1007/978-3-030-40124-5
isbn_softcover978-3-030-40123-8
isbn_ebook978-3-030-40124-5Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2020
The information of publication is updating

書目名稱Radiomics and Radiogenomics in Neuro-oncology影響因子(影響力)




書目名稱Radiomics and Radiogenomics in Neuro-oncology影響因子(影響力)學科排名




書目名稱Radiomics and Radiogenomics in Neuro-oncology網絡公開度




書目名稱Radiomics and Radiogenomics in Neuro-oncology網絡公開度學科排名




書目名稱Radiomics and Radiogenomics in Neuro-oncology被引頻次




書目名稱Radiomics and Radiogenomics in Neuro-oncology被引頻次學科排名




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沙發(fā)
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Opportunities and Advances in Radiomics and Radiogenomics in Neuro-Oncology,to the central nervous system. The biggest clinical challenge in the field currently is to be able to design personalized treatment management solutions in patients based on . knowledge of their survival outcome or response to conventional or experimental treatments. . or the quantitative extraction
地板
發(fā)表于 2025-3-22 04:57:46 | 只看該作者
A Survey on Recent Advancements for AI Enabled Radiomics in Neuro-Oncology, cancer, planning of treatment strategy, and prediction of survival. Radiomics in neuro-oncology has progressed significantly in the recent past. Deep learning has outperformed conventional machine learning methods in most image-based applications. Convolutional neural networks (CNNs) have seen some
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發(fā)表于 2025-3-22 13:52:32 | 只看該作者
cuRadiomics: A GPU-Based Radiomics Feature Extraction Toolkit,ting radiomics features are generally run on CPU only, which leads to large time consumption in situations such as large datasets or complicated task/method verifications. To address this limitation, we have developed a GPU based toolkit namely cuRadiomics, where the computing time can be significan
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發(fā)表于 2025-3-22 18:28:12 | 只看該作者
On Validating Multimodal MRI Based Stratification of IDH Genotype in High Grade Gliomas Using CNNs ype in gliomas from multi-modal brain MRI images. However, it is not yet clear on how well these models can adapt to unseen datasets scanned on various MRI scanners with diverse scanning protocols. Further, gaining insight into the imaging features and regions that are responsible for the delineatio
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發(fā)表于 2025-3-22 22:42:12 | 只看該作者
Imaging Signature of 1p/19q Co-deletion Status Derived via Machine Learning in Lower Grade Glioma,ed by fluorescence in-situ hybridization test is the gold standard currently to identify mutational status for diagnosis and treatment planning, there are several imaging studies to predict the same. Our study aims to determine the 1p/19q mutational status of LGG non-invasively by advanced pattern a
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發(fā)表于 2025-3-23 05:12:00 | 只看該作者
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發(fā)表于 2025-3-23 07:12:35 | 只看該作者
Radiomics-Enhanced Multi-task Neural Network for Non-invasive Glioma Subtyping and Segmentation,-crafted features, so the capacity of capturing comprehensive features from MR images is still limited compared with deep learning method. In this work, we propose a radiomics enhanced multi-task neural network, which utilizes both deep features and radiomic features, to simultaneously perform gliom
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