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English(EN) End-to-end plaque counting and virus titration from laboratory plate images with deep learning

深度学习自动化实验室图像病毒滴定

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一个深度学习系统，用于自动化实验室图像的斑块计数和病毒滴定。该系统使用两个源自Segment Anything Model (SAM) 的模型来分割孔板并检测和计数其中的斑块。该工作流程在各种病毒数据集和培养板格式上进行了评估，结果显示与手动标注和专家评估高度一致，有望减少人工工作量并提高病毒分析的可重复性。 AI

影响 自动化了一个劳动密集型的实验室流程，提高了病毒学研究的可重复性和可扩展性。

排序理由 该集群包含一篇学术论文，详细介绍了一种用于特定科学任务的新型深度学习方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

深度学习自动化实验室图像病毒滴定

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该集群包含一篇学术论文，详细介绍了一种用于特定科学任务的新型深度学习方法。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · José Ignacio Orlando ·

    深度学习实现实验室培养皿图像的端到端斑块计数和病毒滴定

    Plaque assays remain the gold standard readout of virus infectivity; however, plaque counting from plate images is labor-intensive and prone to inter-operator variability. We present an end-to-end, computer-aided workflow for cytopathic effect-based virus titration directly from …

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