电子科技2025，Vol.38Issue(7)：74-81,8.DOI:10.16180/j.cnki.issn1007-7820.2025.07.010
具有缺失信息的甲状腺癌淋巴结转移预测
Prediction of Lymph Node Metastasis in Thyroid Cancer with Missing Information
摘要
Abstract
In the decision-making process for thyroid cancer surgery,the accurate preoperative assessment of lymph node metastasis poses a challenging issue.To minimize unnecessary surgeries and enhance patient quality of life,precise prediction of lymph node metastasis in thyroid cancer is of practical significance.In this study non-neg-ative latent factor model and PEFT(Parameter Efficient Fine Tuning)technique are used to solve the problem of small scale of medical data and missing clinical data.The non-negative latent factor model was used to complete the clinical data to improve the reliability and accuracy of the data.By introducing PEFT technology to fine-tune large pre-trained models,the computational cost is significantly reduced.The results show that the latent factor model is superior to the traditional method under different missing proportions,and the PEFT method has higher training accu-racy and lower training time on two different data sets.By comparing the comprehensive performance of local data set and public data set,the effectiveness of the proposed method is verified.The proposed method reduces the computa-tional cost and has higher interpretability while maintaining high prediction accuracy,and provides an efficient and feasible scheme for the application of pre-trained large models in medical tasks.关键词
潜在因子模型/高效参数微调/PEFT/医学图像处理/数据填补/迁移学习/缺失医学数据/多模态Key words
latent factor model/efficient parameter fine-tuning/PEFT/medical image processing/data filling/transfer learning/lack of medical data/multimodal分类
信息技术与安全科学引用本文复制引用
朱正铭,曾入,宋燕..具有缺失信息的甲状腺癌淋巴结转移预测[J].电子科技,2025,38(7):74-81,8.基金项目
国家自然科学基金(62073223) （62073223）
上海市自然科学基金(22ZR1443400)National Natural Science Foundation of China(62073223) （22ZR1443400）
Natural Science Foundation of Shanghai(22ZR1443400) （22ZR1443400）
