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相关概念视频

Skin Cancer01:30

Skin Cancer

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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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Reducing Line Loss01:18

Reducing Line Loss

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In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
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Deconvolution01:20

Deconvolution

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Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
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相关实验视频

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Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging
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GA-Net:幽灵卷积自适应融合皮肤损伤细分网络.

Longsong Zhou1, Liming Liang2, Xiaoqi Sheng3

  • 1School of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou, Jiangxi, 341000, China; Jinguan Copper Branch of Tongling Nonferrous Metals Group Co, Ltd, Tongling, Anhui, 244100, China.

Computers in biology and medicine
|August 10, 2023
PubMed
概括

这项研究引入了一种新的幽灵卷积自适应融合网络,用于改善医学成像中的皮肤病变细分. 这种新方法通过准确识别病变细节,提高了早期皮肤癌检测.

关键词:
适应性聚变模块适应性聚变模块双边注意力模块 双边注意力模块幽灵的卷合方式层的特征是聚变的聚变.皮肤病变细分 皮肤病变细分

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科学领域:

  • 计算机辅助诊断是一种计算机辅助的诊断.
  • 医疗图像分析 医学图像分析
  • 皮肤病学 皮肤病学

背景情况:

  • 准确的皮肤病变细分对于早期皮肤癌的检测和治疗至关重要.
  • 现有的深度学习方法往往难以提取详细的病变特征,导致不完整的细分.
  • 挑战包括缺少的信息和皮肤病变图像中不准确的细分边界.

研究的目的:

  • 提出一种新的幽灵卷积自适应融合网络,用于增强皮肤病变细分.
  • 为了提高细节性损伤特征的提取和细分精度.
  • 提供一个更有效的工具,用于皮肤疾病的计算机辅助诊断.

主要方法:

  • 嵌入幽灵模块以进行全面的特征提取.
  • 利用自适应融合和双边注意模块来整合浅层和深层网络信息.
  • 采用多级输出模式和层功能融合,以提高像素预测和细分精度.

主要成果:

  • 拟议的网络实现了高精度 (96.42%在ISIC2016上,94.07%在ISIC2017上,95.03%在ISIC2018上) 和卡帕系数.
  • 与公共数据集上的现有网络相比,表现出卓越的性能.
  • 在模拟实验中展示了皮肤病变图像的增强细分结果.

结论:

  • 幽灵卷积自适应融合网络显著提高了皮肤病变细分的准确性.
  • 该方法为计算机辅助诊断和早期发现皮肤癌提供了有希望的进步.
  • 这种方法为皮肤疾病的准确诊断和管理提供了新的可能性.