针对目前胶质瘤影像数据利用率不高、特征提取不全面等问题,提出一种基于深度学习的半监督胶质瘤肿瘤蛋白53(Tumor Protein 53,P53)突变状态预测方法.首先,使用非均匀粒度多批次(Non-Uniform Granularity Multi-Batch,NUGMB)灰度等级划分算法,优化胶质瘤MR影像的预处理;其次,提出多中心协作(Multi Center Collaboration,MCC)的K均值聚类算法,进行胶质瘤影像数据的伪标签标注;最后,提出一种全新的注意力机制LWAM(Local Longer and Wider Attention Modules),构建基于LWAM的改进MaxViT模型,用于胶质瘤P53突变状态术前无创预测.基于NUGMB,MCC和LWAM算法的NML-MaxViT模型预测胶质瘤P53突变状态的准确率为96.23％,可实现术前无创预测,辅助医生的临床诊疗.
Prediction of glioma P53 mutation status based on NML-MaxViT
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.
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