基于内容的组织病理图像检索
Camilo Nuñez-Fernández1, Humberto Farias2, Mauricio Solar1
1Departamento de Informática, Universidad Tecnica Federico Santa Maria, Campus San Joaquin, Santiago 8940897, Chile.
Sensors (Basel, Switzerland)
|March 17, 2025
概括
这项研究引入了一种新的局部-全球特征融合嵌入模型,以改进基因病理图像检索. 该模型通过整合多尺度信息来增强特征描述符,在化学路径24C数据集上实现99.40%的回忆@1.
科学领域:
- 数字病理学数字病理学
- 计算机视觉 计算机视觉
- 医学图像分析 医学图像分析
背景情况:
- 基于内容的图像检索 (CBIR) 系统对于病理学家来说至关重要,但在组织病理学图像中提取特征描述符仍然是一个挑战.
- 当前的深度学习模型往往错过了丰富的空间上下文,因为它们专注于单一尺度的特征.
研究的目的:
- 开发一种改进的方法来提取在基因病理图像中的特性描述符.
- 提高CBIR系统在数字病理学中的嵌入的深度和实用性.
主要方法:
- 提出了局部-全球特征融合嵌入模型,结合了多尺度特征提取骨干.
- 实现了局部-全球特征融合的子分支和基于通用平均值 (GeM) 的聚合头.
- 在ImageNet-1k和PanNuke数据集上训练模型,使用亚中心ArcFace损失.
主要成果:
- 拟议的模型在化学路径24C数据集上实现了99.40%的RECALL@1,用于基因病理图像检索.
- 与最新的方法相比,在补丁级检索中表现出优越的性能.
结论:
- 当地-全球特征融合嵌入模型有效地整合了多个尺度的信息,以进行增强的组织病理图像分析.
- 这种方法显著提高了CBIR系统在数字病理学中的准确性.
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