Implementation of Lung Nodule Detection Model Based on Incremental Meta-Learning
10.12455/j.issn.1671-7104.240100
- VernacularTitle:基于增量元学习的肺结节检测模型设计与实现
- Author:
Zihao ZHANG
1
;
Yuanyuan YANG
Author Information
1. 中国科学院上海技术物理研究所,上海市,200083;中国科学院大学,北京市,100049
- Keywords:
pulmonary nodule;
meta-learning;
incremental learning;
pulmonary CT images
- From:
Chinese Journal of Medical Instrumentation
2024;48(4):355-360
- CountryChina
- Language:Chinese
-
Abstract:
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.
