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

Skin Cancer01:30

Skin Cancer

3.0K
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...
3.0K

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

The Asian tiger mosquito Aedes albopictus (Diptera: Culicidae) in Northern and Central Tunisia: Seasonal activity, spatial distribution modelling and dengue virus surveillance.

Medical and veterinary entomology·2026
Same author

Surveillance of West Nile Virus in Tunisia: Evidence from Human and Entomological Investigation.

Viruses·2025
Same author

Macro- and Micro-Expressions Facial Datasets: A Survey.

Sensors (Basel, Switzerland)·2022
Same author

Towards non-data-hungry and fully-automated diagnosis of breast cancer from mammographic images.

Computers in biology and medicine·2021
Same author

Risk Assessment of the Role of the Ecotones in the Transmission of Zoonotic Cutaneous Leishmaniasis in Central Tunisia.

International journal of environmental research and public health·2021
Same author

Skin lesion image retrieval using transfer learning-based approach for query-driven distance recommendation.

Computers in biology and medicine·2021

相关实验视频

Updated: May 17, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

438

改善皮肤病变的分类,通过突出指导的损失功能.

Rym Dakhli1, Walid Barhoumi2

  • 1Université de Tunis El Manar, Institut Supérieur d'Informatique, Research Team on Intelligent Systems in Imaging and Artificial Vision (SIIVA), LR16ES06, Laboratoire de recherche en Informatique, Modélisation et Traitement de l'Information et de la Connaissance (LIMTIC), 2 Rue Abou Rayhane Bayrouni, Ariana 2080, Tunisia.

Computers in biology and medicine
|May 16, 2025
PubMed
概括

这项研究通过将可解释性 (XAI) 突出度得分纳入损失函数来增强皮肤病变分类的深度学习. 这种新的方法提高了诊断准确性和模型可靠性.

关键词:
深度学习是一种深度学习.损失函数是一个损失函数.量化可解释性评价量化可解释性评价基于 Saliency 的 XAI.皮肤病变的分类 皮肤病变的分类

更多相关视频

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

16.7K
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

2.2K

相关实验视频

Last Updated: May 17, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

438
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

16.7K
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

2.2K

科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 计算机辅助诊断 计算机辅助诊断

背景情况:

  • 深度学习显著推进了医学图像分析,特别是在皮肤病变分类方面.
  • 在医学成像深度学习模型中实现高分类准确性和可解释性仍然存在挑战.

研究的目的:

  • 为了提高深度学习分类器的性能和皮肤病变分类中的可解释性.
  • 引入一种方法,将可解释AI (XAI) 的突出性得分集成到损失函数中.

主要方法:

  • 开发了一个自定义的损失函数,将从XAI突出度得分中获得的惩罚权重纳入其中.
  • 在HAM10000和PH2数据集上使用Inception-ResNet-v2,EfficientNet-B3和ResNeXt分类器评估了该方法.
  • 用各种XAI方法进行测试,以评估它们对分类性能的影响.

主要成果:

  • 在HAM10000上达到94.3%的准确性,在PH2数据集上达到98%的准确性,超过了基线方法.
  • 通过使用LRP指导损失函数而不是标准损失函数,证明了分别提高了7%和6%的准确性.
  • 与最先进的方法相比,展示了相当大的性能提升.

结论:

  • 建议将突出性得分集成到损失函数中,有效地提高了深度学习模型的性能和可靠性.
  • 该方法提供了对XAI技术在改善分类结果方面的有效性的定量评估.
  • 该方法解决了医学成像方面的关键挑战,提供了更可靠的AI驱动诊断工具.