Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Illusion of competence: vision-language models provide confident but inaccurate explanations in cytological diagnostics.

Scientific reports·2026
Same author

The HLH-Risk-Calculator is a machine learning-based tool to predict course & mortality of secondary hemophagocytic lymphohistiocytosis.

Intensive care medicine·2026
Same author

Limited predictive value of PD-L1 TPS for pathological response in NSCLC is not attributable to interobserver variability.

Lung cancer (Amsterdam, Netherlands)·2026
Same author

Increasing TET Expression and 5-Hydroxymethylcytosine Formation by a Carbocyclic 5-Aza-2'-deoxy-cytidine Antimetabolite.

Angewandte Chemie (International ed. in English)·2026
Same author

Histological evaluation of hysterectomy specimens after NovaSure<sup>®</sup> endometrial ablation in patients with atypical endometrial hyperplasia or endometrial carcinoma.

Archives of gynecology and obstetrics·2026
Same author

UNICORN: a deep learning model for integrating multi-stain data in histopathology.

NPJ digital medicine·2026
Same journal

Deutsche medizinische Wochenschrift (1946)·2026
Same journal

["Not everything that looks like a tumor..." - Pulmonary tularemia with hilar lymphadenopathy].

Deutsche medizinische Wochenschrift (1946)·2026
Same journal

[Emergency management of sickle cell disease].

Deutsche medizinische Wochenschrift (1946)·2026
Same journal

[Hereditary dehydrated stomatocytosis (= hereditary xerocytosis) - Interesting hummingbird or clinically relevant diagnosis?]

Deutsche medizinische Wochenschrift (1946)·2026
Same journal

[Diagnosis of Congenital Hemolytic Anemias in Adults].

Deutsche medizinische Wochenschrift (1946)·2026
Same journal

[46-year-old female patient with right upper abdominal pain].

Deutsche medizinische Wochenschrift (1946)·2026
查看所有相关文章

相关实验视频

Updated: Jul 18, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.5K

[人工智能用于计算机辅助白血病诊断]

Christian Matek1,2, Carsten Marr2, Michael von Bergwelt-Baildon3

  • 1Pathologisches Institut, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Deutschland.

Deutsche medizinische Wochenschrift (1946)
|August 23, 2023
PubMed
概括
此摘要是机器生成的。

人工智能 (AI) 通过分析血液和骨髓样本来帮助白血病诊断,在细胞识别方面达到人类水平的性能. 可解释的人工智能和集成诊断有望提高准确性,但需要大量数据.

更多相关视频

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence
09:11

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Published on: January 27, 2023

2.2K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

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

Published on: August 16, 2020

6.9K

相关实验视频

Last Updated: Jul 18, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.5K
Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence
09:11

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Published on: January 27, 2023

2.2K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

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

Published on: August 16, 2020

6.9K

科学领域:

  • 计算病理学计算病理学
  • 血液学 血液学 血液学
  • 人工智能在医学中的应用

背景情况:

  • 对白血病的血液和骨髓的手动审查是主观的,劳动密集的.
  • 准确的白血病诊断需要高质量的数字化样本和大型注释数据集用于AI开发.
  • 当前的人工智能方法有前途,但在数据要求和可解释性方面面临挑战.

研究的目的:

  • 探索人工智能,特别是深度学习在白血病诊断中的应用.
  • 讨论可解释的人工智能和综合诊断在提高准确性和透明度方面的作用.
  • 突出临床AI实施的数据要求和验证需求.

主要方法:

  • 开发深度学习算法,用于分析数字化血液和骨髓样本.
  • 利用多个实例学习从白细胞收集的诊断预测.
  • 实施可解释的AI技术,以提高预测透明度和用户验证.

主要成果:

  • 深度学习模型在特定任务上达到人类水平的性能,例如爆细胞表征.
  • 多实例学习方法显示出诊断的潜力,但数据密集度更高.
  • 可解释的AI提高了透明度,允许对算法预测进行验证.

结论:

  • 人工智能算法提供了一个强大的工具来增强白血病诊断,提高效率和潜在的准确性.
  • 强度和稳定性分析对于AI诊断工具的临床采用至关重要.
  • 综合诊断和更大,多样化的数据集是推动血液恶性瘤人工智能的关键.