基于增量元学习的肺结节检测模型设计与实现  

Implementation of Lung Nodule Detection Model Based on Incremental Meta-Learning

作　　者：张子豪 杨媛媛[1] ZHANG Zihao;YANG Yuanyuan(Shanghai Institution of Technical Physics,Chinese Academy of Sciences,Shanghai,200083;University of Chinese Academy of Sciences,Beijing,100049)

机构地区：[1]中国科学院上海技术物理研究所,上海市200083 [2]中国科学院大学,北京市100049

出　　处：《中国医疗器械杂志》2024年第4期355-360,共6页Chinese Journal of Medical Instrumentation

摘　　要：针对传统肺结节检测模型无法随新数据增加而动态优化更新的问题,提出了一种新的肺结节检测模型——任务增量元学习模型(TIMLM)。该模型由内外2个循环构成。内循环设置了增量学习正则化更新约束,而外循环通过元更新策略对新旧知识进行采样并学习一组适应新旧数据的广义参数。在不改变模型主体结构的前提下,TIMLM尽可能地保留了之前学到的旧知识。通过在公开的肺部数据集上开展实验验证,结果表明,相较于传统的深度网络模型和主流的增量学习模型,TIMLM在准确度和敏感度等指标上都有显著提升,展现出良好的持续学习和抗遗忘能力。In response to the issue that traditional lung nodule detection models cannot dynamically optimize and update with the increase of new data,a new lung nodule detection model—task incremental meta-learning model(TIMLM)is proposed.This model comprises of two loops:the inner loop imposes incremental learning regularization update constraints,while the outer loop employs a meta-update strategy to sample old and new knowledge and learn a set of generalized parameters that adapt to old and new data.Under the condition that the main structure of the model is not changed as much as possible,it preserves the old knowledge that was learned previously.Experimental verification on the publicly available lung dataset showed that,compared with traditional deep network models and mainstream incremental models,TIMLM has greatly improved in terms of accuracy,sensitivity,and other indicators,demonstrating good continuous learning and anti-forgetting capabilities.

关 键 词：肺结节 元学习 增量学习 肺部CT图像 

分 类 号：R734.2[医药卫生—肿瘤] TP391.41[医药卫生—临床医学]