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相关概念视频

Classification of Leukocytes01:30

Classification of Leukocytes

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Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...
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Radiological investigations, including X-rays and computed tomography (CT) scans, are critical for diagnosing and evaluating various medical conditions. These imaging techniques provide valuable insights into the body's internal structures, aiding in the detection of abnormalities, assessment of disease progression, and development of treatment strategies. This article delves into two primary radiological investigations, chest X-rays and CT scans, outlining their purpose, procedures, and...
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相关实验视频

Updated: Jul 25, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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使用监督学习的放射学报告的多语言RECIST分类.

Luc Mottin1,2, Jean-Philippe Goldman3, Christoph Jäggli4

  • 1HES-SO\HEG Genève, Information Sciences, Geneva, Switzerland.

Frontiers in digital health
|June 30, 2023
PubMed
概括

这项研究探讨了AI和NLP用于从放射学报告中自动进行RECIST分类. 最好的模型实现了高精度,可与专家标签相提并论,并且对新数据进行了很好的概括.

关键词:
拒绝,反对,反对.语言模型语言模型叙事文本的分类 叙事文本的分类放射学报告 放射学报告监督机器学习是指监督机器学习.

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科学领域:

  • 医疗信息学 医疗信息学
  • 人工智能的人工智能
  • 自然语言处理自然语言处理.

背景情况:

  • 准确的瘤反应评估对于癌症治疗至关重要.
  • 手动从放射学报告中进行RECIST分类可能是耗时且主观的.
  • 自动化RECIST分类可以提高效率和一致性.

研究的目的:

  • 探索人工智能和NLP技术用于自动RECIST分类.
  • 评估语言 (法语,德语) 和机构因素对分类质量的影响.
  • 评估开发模型的概括性和准确性.

主要方法:

  • 评估了7种用于基线性能的机器学习方法.
  • 为法语和德语语言开发和微调模型.
  • 将模型性能与专家注释进行比较,使用F1得分,马修的相关系数和科恩的卡帕等指标.

主要成果:

  • 在2类 (进步/非进步) 中获得了90%的F1平均分数,在4类RECIST分类中获得了86%的F1分数.
  • 性能指标与手动标签具有竞争力 (MCC 79%,卡帕 76%).
  • 在未见的数据上证明模型的概括性.

结论:

  • 人工智能和NLP技术有效地支持自动RECIST分类.
  • 语言和机构的特殊性对分类质量有可管理的影响.
  • 预训练的语言模型 (PLM) 可以提高分类器的准确性.