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通过使用多实例学习,SMMILe可以在数字病理学中准确量化空间.

Zeyu Gao1,2, Anyu Mao3, Yuxing Dong3

  • 1Department of Oncology, University of Cambridge, Cambridge, UK.

Nature cancer
|November 19, 2025
PubMed
概括

本研究介绍了SMMILe,这是一种新的计算病理学方法,可以在整个幻灯片图像中增强空间量化,而不会牺牲分类准确性. 在数字病理学中,SMMILe 提高了诊断预测和空间意识.

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Methods of Medium Optimization01:28

Methods of Medium Optimization

Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...
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