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相关概念视频

Tumor Progression02:07

Tumor Progression

Tumor progression is a phenomenon where the pre-formed tumor acquires successive mutations to become clinically more aggressive and malignant. In the 1950s, Foulds first described the stepwise progression of cancer cells through successive stages.
Colon cancer is one of the best-documented examples of tumor progression. Early mutation in the APC gene in colon cells causes a small growth on the colon wall called a polyp. With time, this polyp grows into a benign, pre-cancerous tumor. Further...
Tumor Progression02:07

Tumor Progression

Tumor progression is a phenomenon where the pre-formed tumor acquires successive mutations to become clinically more aggressive and malignant. In the 1950s, Foulds first described the stepwise progression of cancer cells through successive stages.
Colon cancer is one of the best-documented examples of tumor progression. Early mutation in the APC gene in colon cells causes a small growth on the colon wall called a polyp. With time, this polyp grows into a benign, pre-cancerous tumor. Further...

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相关实验视频

Updated: Jun 20, 2026

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口腔状细胞癌分级分类使用深度变压器编码器辅助扩展卷积与全球关注.

Singaraju Ramya1, R I Minu1

  • 1Department of Computing Technologies, School of Computing, SRM Institute of Science and Technology, Chennai, India.

Frontiers in artificial intelligence
|November 3, 2025
PubMed
概括

一个新的深度变压器编码器辅助扩展卷积与全球注意力 (DeTr-DiGAtt) 模型提高了口腔状细胞癌 (OSCC) 的分类准确性. 这种由人工智能驱动的方法增强了图像分析,以获得更好的患者结果.

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:

  • 口腔状细胞癌 (OSCC) 具有显著的发病率和死亡率.
  • 现有的OSCC分类方法,如AlexNet和CNN在准确性和数据处理方面存在局限性.
  • 准确的OSCC分级对于有效的治疗计划至关重要.

研究的目的:

  • 引入一种新的深度变压器编码器辅助扩展卷积与全球注意力 (DeTr-DiGAtt) 模型,以增强OSCC分类.
  • 解决当前方法的局限性,包括精度低,数据稀缺和长时间的培训时间.
  • 为了提高OSCC分级的准确性和效率.

主要方法:

  • 使用生成对抗网络 (GAN) 进行数据增强以减轻过度匹配.
  • 使用自适应双边过器 (Ad-BF) 进行图像预处理和降噪.
  • 实施了一种改进的多编码器残余挤压U-Net (Imp-MuRs-Unet) 来精确细分受影响地区.
  • 应用了DeTr-DiGAtt模型用于OSCC分级,并使用自适应灰色滞后优化算法 (Ad-GreLop) 进行了优化.

主要成果:

  • 德特-迪加特模型实现了98.59%的高精度 (ACC).
关键词:
在 GAN 模型中.灰色滞后优化算法和全球关注在U-net模型中,适应性双边过器 适应性双边过器扩张的卷积性卷积.

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  • 显示出出色的表现,子得分为97.97%和十字路口超过联盟 (IoU) 的98.08%.
  • 综合方法显著提高了图像质量,细分精度和分类性能.

    • 结论:

    • 与现有方法相比,拟议的DeTr-DiGAtt模型为OSCC分类提供了优越的解决方案.
    • 数据增强,图像过,高级细分和优化深度学习的结合显著提高了诊断能力.
    • 这种人工智能驱动的框架有望改善口腔状细胞癌的早期检测和准确分类.