适应性Bandelet转换和转移学习为几何意识的甲状腺癌超声波分类
Yassine Habchi1, Hamza Kheddar2, Mohamed Chahine Ghanem3
1Faculty of Technology, University Salhi Ahmed, Naama 45000, Algeria.
Diagnostics (Basel, Switzerland)
|February 27, 2026
概括
这项研究提高了甲状腺结节的分类,使用了适应几何的Bandelet Transform (BT) 和转移学习 (TL). 综合方法显著提高了超声波图像分析的准确性和效率.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 生物医学工程 生物医学工程
背景情况:
- 由于数据有限和纹理复杂性,在超声波中对甲状腺结节进行分类是困难的.
- 传统的方法在超声波图像中难以捕捉复杂的,多方向的纹理.
研究的目的:
- 为了提高数据效率的甲状腺结节分类.
- 通过使用Bandelet转换 (BT) 和转移学习 (TL) 来增强特征表示和概括.
主要方法:
- 应用了适应几何的带列转换 (BT) 来增强方向和结构编码.
- 通过SMOTE减轻了阶级不平衡,并通过增强增加了数据多样性.
- 使用 ImageNet 预训练架构进行分类的特征,其中 VGG19 显示出最佳性能.
主要成果:
- 在各种值的波段表示上,BT预处理提高了性能.
- 该BT+TL (VGG19) 模型实现了98.91%的精度,98.11%的灵敏度,97.31%的特异性和98.89%的F1分数.
- 在DDTI数据集上表现优于可比方法.
结论:
- 将几何适应变换与TL骨干相结合,为甲状腺结节分类提供了一个强大的,数据效率高的策略.
- 这种方法在有限的注释和复杂的纹理方面尤其有效.
- 项目和代码是公开的.
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02:53Adaptive Mechanisms in Cancer Cells
Cancer cells accumulate genetic changes at an abnormally rapid rate due to the defects in the DNA repair mechanisms. From an evolutionary perspective, such genetic instability is advantageous for cancer development. Mutant cell lines accumulate a series of beneficial mutations that contribute to their progression into cancer.
Some of the advantages that cancer cells have on normal cells include - enhanced ability to divide without terminally differentiating, induce new blood vessel formation,...
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