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Deep Learning of Tissue Fate Features in Acute Ischemic Stroke
In acute ischemic stroke treatment, prediction of tissue survival outcome plays a fundamental role in the clinical decision-making process, as it can be used to assess the balance of risk vs. possible benefit when considering endovascular clot-retrieval intervention. For the first time, we construct...
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| 出版年: | Proceedings (IEEE Int Conf Bioinformatics Biomed) |
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| 主要な著者: | , , , |
| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
2015
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| 主題: | |
| オンライン･アクセス: | https://ncbi.nlm.nih.gov/pmc/articles/PMC5597003/ https://ncbi.nlm.nih.gov/pubmed/28919983 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1109/BIBM.2015.7359869 |
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