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戈尔斯:使用层次学习架构和多维特征聚合来进行概括的风险器官细分.

Xuezheng Sun1, Tao Wan1, Jiankun Xu2

  • 1School of Biomedical Science and Medical Engineering, Beihang University, Beijing, 100083, China; Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, 100083, China.

Computers in biology and medicine
|November 27, 2025
PubMed
概括

一种新的一般化风险器官 (OARs) 分段方法 (GOARS) 提高了放射治疗规划的准确性. 这种自动化解决方案精确地对大和小器官进行细分,克服了当前深度学习方法的局限性.

关键词:
功能聚合 功能聚合.阶层式的学习学习.多机关细分化多机关细分化处于危险中的器官放射疗法是一种放射治疗.

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Aggregates Classification01:29

Aggregates Classification

Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
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