医学影像组学特征选择与分类模型的工程化实现 ——基于 feature engineering特征工程的 AI平台设计实践
摘要
究围绕高维小样本医学影像数据,从需求约束、系统模块与工程落地三方面展开,构建统一数据底座、可组合特征
筛选链路、标准化模型训练接口,并配套日志追踪与一键式自动化流程。结果表明,影像组学平台的核心价值在于
流程稳定、数据可追溯、结果可复核,而非单纯堆叠算法。该研究为医学AI科研平台研发、工程化落地及产品化迭
代提供可复用的设计思路与技术参考。
关键词
全文:
PDF参考
[1]WOZNICKI P, SARRAZIN O, MUIR J, et al.
AutoRadiomics: A framework for reproducible radiomics
research[J]. Frontiers in Radiology, 2022, 2:919133.
[2]DEWI D E O, SUNOQROT M R S, NKETIAH
G A, et al. The impact of pre-processing and disease
characteristics on reproducibility of T2-weighted MRI
radiomics features[J]. Magnetic Resonance Materials in
Physics, Biology and Medicine, 2023, 36:945-956.
[3]SHOEMAKER K, GER R, COURT L E, et al.
Bayesian feature selection for radiomics using reliability
metrics[J]. Frontiers in Genetics, 2023, 14:1112914.
[4]HANIFF N S M, NG K H, KAMAL I, et al.
Systematic review and meta-analysis on the classification
metrics of machine learning algorithm based radiomics
in hepatocellular carcinoma diagnosis[J]. Heliyon, 2024,
10(16):e36313.
[5]MORADMAND H, MOLITORIS J, LING X, et al.
Graph feature selection for enhancing radiomic stability and
reproducibility across multiple institutions in head and neck
cancer[J]. Scientific Reports, 2025, 15:27995.
Refbacks
- 当前没有refback。
