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动态特征拼接用于几次射击罕见疾病诊断

Yuanyuan Chen¹, Xiaoqing Guo², Yongsheng Pan¹

¹National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an 710072, China.

Medical image analysis

|September 27, 2023

概括

识别具有少数射击学习 (FSL) 的罕见疾病是具有挑战性的. 本研究引入了一个动态特征拼接 (DNFS) 框架,通过从有限的数据中丰富特征来改进少数射击的罕见疾病诊断.

科学领域:

医疗成像医学成像
机器学习机器学习
计算机视觉计算机视觉

背景情况:

获得注释医疗图像用于罕见疾病诊断是很困难的.
短暂学习 (FSL) 旨在识别具有有限数据的罕见疾病.
现有的FSL方法与复杂的医疗图像特征和高类内差异作斗争.

研究的目的:

提出一种新的动态特征拼接 (DNFS) 框架,用于为数不多的罕见疾病诊断.
通过丰富的基础类的知识,增强罕见疾病类的低级和高级特征.
为了提高罕见疾病诊断的准确性和性能,在几次射击的环境中.

主要方法:

开发了一个动态特征拼接 (DNFS) 框架,包含位置连贯 (P-DNFS) 和语义连贯 (S-DNFS) 模块.
P-DNFS使用面向病变的变压器来检测低级特征拼接的病变区域.
S-DNFS 探索基于语义一致性的高层特征拼接的交叉图像通道关系.

主要成果:

该DNFS框架动态和代丰富低级和高级特征.
产生了丰富的拼接特征,导致更准确的决策边界.
与最先进的方法相比,在三个医学图像分类数据集上表现出优异的性能.

结论:

拟议的DNFS框架有效地应对了几次射击罕见疾病诊断的挑战.
动态特征拼接通过利用基础类的知识显著提高了诊断准确性.
在资源有限的情况下,DNFS提供了一种有希望的方法来识别罕见疾病.

关键词:

频道相似性频道相似性功能拼接功能拼接有几次射击学习学习.罕见疾病诊断诊断罕见疾病诊断变压器变压器变压器

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