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相关概念视频

Lymphoid Cells and Tissues01:18

Lymphoid Cells and Tissues

1.5K
Lymphoid cells and tissues are integral to the immune system, which is crucial in maintaining our body's defense against harmful pathogens. They form the building blocks of lymphoid organs, which include the spleen, thymus, and lymph nodes.
Lymphoid cells consist of various types of immune system cells. These include B and T lymphocytes, which are responsible for producing antibodies and killing infected cells, respectively. Dendritic cells act as messengers between the innate and adaptive...
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Classification of Epithelial Tissues: Overview01:22

Classification of Epithelial Tissues: Overview

14.9K
Epithelial tissues are classified according to the shape of the cells and the number of cell layers formed. Cell shapes can be squamous (flattened and thin), cuboidal (square-like, as wide as it is tall), or columnar (rectangular, taller than it is wide). Additionally, the nucleus shape helps identify the type of epithelial cells. Squamous cells have flattened disc-shaped nuclei, cuboidal cells have spherical nuclei, and columnar cells have elongated nuclei.
Based on the number of cell layers,...
14.9K
Classification of Leukocytes01:30

Classification of Leukocytes

2.7K
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...
2.7K
Classification of Connective Tissues01:30

Classification of Connective Tissues

11.5K
The connective tissues have different properties and functions in the human body. They are broadly categorized into proper, supporting, or fluid connective tissues.
Connective Tissue Proper
Connective tissue proper is the most abundant class of connective tissues. As its name implies, it predominantly connects different tissues in the body. Depending on the cell types, ground substance, viscosity, and fiber types in the ECM, connective tissue proper is further categorized into loose and dense....
11.5K
Classification of Epithelial Tissues: Glandular Epithelium01:20

Classification of Epithelial Tissues: Glandular Epithelium

9.6K
The glandular epithelium is made of one or more epithelial cells modified to synthesize and secrete chemical substances. Glandular epithelia can be classified based on cell number. Unicellular glands have individual secretory cells scattered across the epithelial monolayer. In contrast, multicellular glands consist of a hollow tubular duct attached to the cluster of secretory cells located in the deep pockets.
Multicellular glands are formed during early development when epithelial budding...
9.6K

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相关实验视频

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Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
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使用深度学习和可解释的人工智能 (XAI) 在毛囊淋巴瘤和反应性淋巴细胞组织之间进行组织学图像分类.

Joaquim Carreras1, Haruka Ikoma1, Yara Yukie Kikuti1

  • 1Department of Pathology, School of Medicine, Tokai University, 143 Shimokasuya, Isehara 259-1193, Kanagawa, Japan.

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概括
此摘要是机器生成的。

卷积神经网络 (CNN) 在淋巴结活检中准确地将卵泡淋巴瘤与反应性淋巴组织区分开来. 这种人工智能 (AI) 工具表现出高性能,帮助病理学家进行具有挑战性的诊断.

关键词:
人工智能的人工智能是人工智能.卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.可解释的人工智能毛囊过度增生 毛囊过度增生毛囊性淋巴瘤是一种毛囊性淋巴瘤反应性淋巴细胞组织.

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科学领域:

  • 计算病理学计算病理学
  • 人工智能在瘤学中的应用
  • 数字病理学数字病理学

背景情况:

  • 鉴别良性和恶性淋巴结活检对于病理学家来说至关重要.
  • 将毛囊淋巴瘤与反应性淋巴组织区分开来,带来了诊断上的挑战.
  • 血素和欧 (H&E) 染色是组织病理学分析的标准.

研究的目的:

  • 开发和评估一个卷积神经网络 (CNN) 用于分类卵泡淋巴瘤.
  • 用H&E染色淋巴结活检来比较CNN性能与反应性淋巴组织.
  • 使用可解释AI (XAI) 方法评估AI模型的可解释性.

主要方法:

  • 基于ResNet的CNN在221例病例 (177例卵泡淋巴瘤,44例反应性淋巴组织) 的数据集上进行了设计和训练.
  • 数据集包括超过150万个图像补丁,分为培训,验证和测试集.
  • 可解释的AI技术,包括 grad-CAM,图像LIME和遮蔽灵敏度,用于模型解释性.

主要成果:

  • 在测试组上,CNN在区分毛囊淋巴瘤与反应性淋巴组织方面取得了高准确率 (99.80%).
  • 观察到优秀的性能指标:精度 (99.8%),回忆 (99.8%),特异性 (99.7%) 和F1得分 (99.9%).
  • 患者级验证证实了该模型的强大分类性能,最大限度地减少了信息泄露.

结论:

  • 特定任务的人工智能 (AI) 在卵泡淋巴瘤的差异诊断中表现出有效性.
  • 经过培训的ResNet CNN显示了转移学习在更广泛的淋巴瘤诊断应用中的潜力.
  • 人工智能工具可以帮助病理学家,但在特定诊断任务的定义限制内运行.