An Intelligent Diagnosis Method for NIID Based on Cross Self-supervision and DWI

doi:10.11938/cjmr20243136

Abstract

Abstract:

Neuronal intranuclear inclusion disease (NIID) is a rare neurodegenerative disease primarily diagnosed through diffusion-weighted imaging (DWI). However, the limitation of human visual interpretation and clinical experience can lead to inaccuracies in diagnosis. This research proposes a deep learning method based on cross self-supervision, alongside the construction of Co-ResNet50 and CO-ViT models for intelligent auxiliary diagnosis of NIID. This method uses self-supervised learning and effectively combines the characteristics of ResNet50 and ViT networks to improve the model’s feature extraction capabilities. The experiment preprocessed 249 DWI data and divided them into 204 training sets and 45 test sets. The results reveal that the CO-ResNet50 model has the best performance, with an accuracy of 95.49%, precision of 95.51%, recall of 95.44%, F1 score of 0.954 7, and AUC of 0.989 7. These findings underscore the model's potential to provide support for clinical NIID diagnosis.

Key words: MRI, NIID, self-supervised learning, intelligent diagnosis, deep learning

CLC Number:

O482.53

CAO Fei, XU Qianqian, CHEN Hao, ZU Jie, LI Xiaowen, TIAN Jin, BAO Lei. An Intelligent Diagnosis Method for NIID Based on Cross Self-supervision and DWI[J]. Chinese Journal of Magnetic Resonance, 2025, 42(2): 154-163.

Figures/Tables 9

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特征	训练集	测试集
样本数量	204	45
年龄/岁	64.92±9.88	60.47±11.91
男性/女性	86/118	22/23

仪器	重复时间/ms	回波时间/ms	图像分辨率	层厚/mm	翻转角
Philips 3.0T	2820.82	80.45	256×256×18	5	90°
GE 3.0T	4880.00	78.10	256×256×18	6	90°
TOSHIBA 3.0T	4000.00	100.00	256×256×18	6	90°

方法	准确率/%	精确率/%	召回率/%	F1分数	AUC
ViT	82.75	82.80	82.55	0.8263	0.9304
ResNet50	86.86	87.19	86.57	0.8673	0.9625
MAE_ViT	90.78	91.26	90.49	0.9068	0.9729
CO-ViT	91.37	91.76	91.11	0.9129	0.9704
CO-ResNet50	95.49	95.51	95.44	0.9547	0.9897