南京大学学报（自然科学版）2024，Vol.60Issue(6)：908-919,12.DOI:10.13232/j.cnki.jnju.2024.06.003
基于NML-MaxViT的胶质瘤P53突变状态预测
Prediction of glioma P53 mutation status based on NML-MaxViT
摘要
Abstract
A deep learning-based semi-supervised prediction method for the P53 mutation status of glioma is proposed to address the current problems of poor utilisation of glioma image data and incomplete feature extraction.Firstly,NUGMB(Non-Uniform Granularity Multi-Batch)grey level partitioning algorithm is proposed to optimize the preprocessing methods of glioma MR image.Secondly,the K-means clustering algorithm of MCC(Multi Center Collaboration)is proposed for pseudo-labeling of glioma image data.Finally,a novel attention mechanism,LWAM(Local Longer and Wider Attention Modules),is proposed to construct an improved MaxViT model based on LWAM for the preoperative non-invasive prediction of the P53 mutation status of glioma.The NML-MaxViT model based on NUGMB,MCC and LWAM algorithms predicts the P53 mutation status of glioma with an accuracy of 96.23％,which achieves non-invasive predictions to assist physicians in clinical diagnosis and treatment.关键词
脑胶质瘤/P53/伪标签/非均匀灰度等级划分/注意力机制改进Key words
glioma/P53/pseudo-labelling/non-uniform gray scale division/attention mechanism improvement分类
医药卫生引用本文复制引用
梁峰宁,赵钰琳,赵藤,曹亚茹,丁世飞,朱红..基于NML-MaxViT的胶质瘤P53突变状态预测[J].南京大学学报（自然科学版）,2024,60(6):908-919,12.基金项目
国家自然科学基金(62102345),江苏省卫生健康委医学科研项目(Z2020032),徐州市重点研发计划(KC22117) （62102345）
