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相关概念视频

Classification of Illness01:17

Classification of Illness

The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe and...
Classification of Epithelial Tissues: Overview01:22

Classification of Epithelial Tissues: Overview

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,...
Classification of Epithelial Tissues: Stratified Epithelium01:29

Classification of Epithelial Tissues: Stratified Epithelium

Stratified epithelium consists of several stacked layers of cells. They provide the durability to withstand constant physical and chemical attacks. Stratified epithelium is named after the shape of the most apical layer of cells. Stratified squamous epithelium is the most common type found in the human body. In this tissue, the apical cells are squamous, whereas the basal layer contains either columnar or cuboidal cells. The basal cells divide to form new daughter cells, which gradually become...
Classification of Connective Tissues01:30

Classification of Connective Tissues

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.
Classification of Leukocytes01:30

Classification of Leukocytes

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...
Imaging Studies III: Gastrointestinal Motility Studies and Virtual Colonoscopy01:26

Imaging Studies III: Gastrointestinal Motility Studies and Virtual Colonoscopy

This lesson explores three gastrointestinal imaging techniques: radionuclide testing, colonic transit studies, and virtual colonoscopy.
Radionuclide Testing
Radionuclide testing is a sophisticated medical technique for assessing gastrointestinal motility. It focuses on gastric emptying and colonic transit time. Radioactive markers track the movement of food through the digestive system, providing insights into gastrointestinal disorders.
In gastric emptying studies, a meal's liquid and solid...

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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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一个半监督的学习框架,以根据NICE分类来分类结直肠新生病.

Yu Wang1, Haoxiang Ni2,3, Jielu Zhou2,4

  • 1Department of Hepatobiliary Surgery, Jintan Affiliated Hospital of Jiangsu University, Changzhou, Jiangsu, 213200, China.

Journal of imaging informatics in medicine
|April 23, 2024
PubMed
概括

使用SimCLR进行半监督学习显著改善了从内镜图像中对结直肠瘤的分类. 这种方法通过使用有限的标记数据来提高模型性能,优于传统方法并有助于早期检测.

关键词:
结肠直肠部分 结肠直肠部分计算机辅助诊断是一种计算机辅助的诊断.深度学习是一种深度学习.这是Grad-CAM.根据NBI国际结直肠内镜 (NICE) 分类.自主监督学习 (SSL)简单的框架用于对比学习的视觉表示 (SimCLR).有监督的学习学习.这就是T-SNENE.

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科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 在瘤学瘤学.

背景情况:

  • 医疗图像标签是资源密集型,需要专业知识和大型数据集.
  • 标记数据不足阻碍了监督学习模型的性能,导致不足.

研究的目的:

  • 开发基于SimCLR的半监督学习框架,使用NICE分类来分类结直肠瘤.
  • 评估框架的性能与监督转移学习和人类内镜师相比.

主要方法:

  • 在未标记的数据上使用自主监督学习训练了一个支持ResNet的SimCLR模型.
  • 在有限的标记数据集上对模型进行了微调,用于NICE分类.
  • 使用精度,马修的相关系数 (MCC) 和科恩的卡帕,用Grad-CAM和t-SNE进行可视化来评估性能.

主要成果:

  • SimCLR模型实现了高精度 (0.908),MCC (0.862) 和科恩的卡帕 (0.896),超过了监督转移学习和初级内镜师.
  • 该模型的表现与高级内镜师的表现相当.
  • 与监督转移学习相比,t-SNE可视化显示了自我监督学习的样本集群优越.

结论:

  • 半监督学习,特别是SimCLR,为在医疗图像分析中使用有限的标记数据进行深度学习提供了强大的解决方案.
  • 这一框架显示了提高结直肠瘤分类的准确性和效率的潜力.
  • 该研究强调了自我监督预训练的优点,以提高深度学习模型的解释性和内镜成像中的性能.