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Titlebook: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries; 6th International Wo Alessandro Crimi,Spyridon Bakas Conferen

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發(fā)表于 2025-3-21 17:15:02 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries
期刊簡稱6th International Wo
影響因子2023Alessandro Crimi,Spyridon Bakas
視頻videohttp://file.papertrans.cn/191/190324/190324.mp4
學(xué)科分類Lecture Notes in Computer Science
圖書封面Titlebook: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries; 6th International Wo Alessandro Crimi,Spyridon Bakas Conferen
影響因子.This two-volume set LNCS 12658 and 12659 constitutes the thoroughly refereed proceedings of the 6th International MICCAI Brainlesion Workshop, BrainLes 2020, the International Multimodal Brain Tumor Segmentation (BraTS) challenge, and the Computational Precision Medicine: Radiology-Pathology Challenge on Brain Tumor Classification (CPM-RadPath) challenge. These were held jointly at the 23rd Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2020, in Lima, Peru, in October 2020.*..The revised selected papers presented in these volumes were organized in the following topical sections: brain lesion image analysis (16 selected papers from 21 submissions); brain tumor image segmentation (69 selected papers from 75 submissions); and computational precision medicine: radiology-pathology challenge on brain tumor classification (6 selected papers from 6 submissions)...*The workshop and challenges were held virtually..
Pindex Conference proceedings 2021
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Monomeric and Polymeric Carboxylic Acids, changes may indicate poor prognosis despite lower grade histology, such as findings consistent with two grade IV infiltrating gliomas: molecular glioblastoma and diffuse midline gliomas. Detailed molecular characterization aids in optimization of treatment with the goal of improved patient outcomes.
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Macromolecular Protein Complexes Ved data through the regularization branch in a semi-supervised manner. We experimentally show that our proposed method outperforms other multi-task methods including the state-of-the-art fully supervised model when the amount of annotated data is limited.
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