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相关概念视频

Skin Cancer01:30

Skin Cancer

4.2K
Skin cancer is a type of cancer that occurs when there is an abnormal growth of skin cells, usually triggered by damage to the DNA within the skin cells. It is primarily caused by exposure to ultraviolet (UV) radiation from the sun or artificial sources like tanning beds. Skin cancer is the most common type of cancer worldwide, and its incidence continues to rise.
Basal Cell Carcinoma (BCC): BCC is the most common type of skin cancer, accounting for about 80% of cases. It typically develops in...
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Renewal of Skin Epidermal Stem Cells01:12

Renewal of Skin Epidermal Stem Cells

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The skin is divided into epidermis, dermis, and hypodermis, the skin's outermost, middle, and inner layers. The human epidermal layer regularly undergoes renewal, where old, dead cells are replaced by new cells. Epidermal stem cells or EpiSCs divide and differentiate to restore the lost cells. For the renewal process, some EpiSCs continuously self-renew. In contrast, few others differentiate into transit-amplifying cells, which later form prickle or spinous cells, followed by granular...
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相关实验视频

Updated: Jul 27, 2025

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition
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Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition

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使用深度学习检测皮肤癌症-一篇评论

Maryam Naqvi1, Syed Qasim Gilani2, Tehreem Syed3

  • 1Institute of Digital Anti-Aging Healthcare, Inje University, Gimhae 50834, Republic of Korea.

Diagnostics (Basel, Switzerland)
|June 10, 2023
PubMed
概括

使用深度学习的早期皮肤癌检测可以提高诊断准确度和生存率. 本综述强调了深度学习模型和数据集的最新进展,以有效地分类皮肤癌.

关键词:
这是分类分类的分类.深度学习是一种深度学习.细分化 细分化的细分化皮肤癌是皮肤癌.

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Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging
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Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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相关实验视频

Last Updated: Jul 27, 2025

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition
09:37

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition

Published on: August 18, 2022

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Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging
06:08

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Published on: May 5, 2011

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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科学领域:

  • 在瘤学瘤学.
  • 医疗成像医学成像
  • 人工智能的人工智能

背景情况:

  • 皮肤癌是全球重要的健康问题,也是全球癌症相关死亡的主要原因.
  • 目前的诊断方法,主要是视觉检查,在准确性方面存在局限性,这强调了需要改进检测策略的需要.
  • 早期诊断对于降低与皮肤癌相关的死亡率至关重要.

研究的目的:

  • 调查皮肤癌分类深度学习方法的最新进展.
  • 为皮肤学研究中常用的深度学习模型提供概述.
  • 确定和讨论在皮肤癌分类算法培训和验证中使用的流行的数据集.

主要方法:

  • 系统审查最近的研究文章,重点关注皮肤癌分类的深度学习.
  • 分析常用的深度学习架构 (例如卷积神经网络).
  • 检查用于皮肤癌图像分析的公开可用和专有数据集.

主要成果:

  • 与传统方法相比,深度学习模型在提高皮肤癌诊断的准确性方面具有显著的潜力.
  • 卷积神经网络经常被使用,在分类各种皮肤病变类型方面表现出很高的性能.
  • 多样化的数据集的可用性和质量对于深度学习模型的概括性和稳定性至关重要.

结论:

  • 深度学习为改善早期和准确的皮肤癌诊断提供了一个有希望的途径.
  • 进一步研究和开发深度学习模型和精心策划的数据集对于临床翻译至关重要.
  • 人工智能辅助的诊断工具可以支持皮肤科医生,可能导致更好的患者结果和降低医疗保健成本.