首页|Ensemble Attention Guided Multi-SEANet Trained with Curriculum Learning for Noninvasive Prediction of Gleason Grade Groups from MRI

Ensemble Attention Guided Multi-SEANet Trained with Curriculum Learning for Noninvasive Prediction of Gleason Grade Groups from MRI

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The Gleason grade group(GG)is an important basis for assessing the malignancy of prostate can-cer,but it requires invasive biopsy to obtain pathology.To noninvasively evaluate GG,an automatic prediction method is proposed based on multi-scale convolutional neural network of the ensemble attention module trained with curriculum learning.First,a lesion-attention map based on the image of the region of interest is proposed in combination with the bottleneck attention module to make the network more focus on the lesion area.Second,the feature pyramid network is combined to make the network better learn the multi-scale information of the lesion area.Finally,in the network training,a curriculum based on the consistency gap between the visual evaluation and the pathological grade is proposed,which further improves the prediction performance of the network.Ex-perimental results show that the proposed method is better than the traditional network model in predicting GG performance.The quadratic weighted Kappa is 0.4711 and the positive predictive value for predicting clinically significant cancer is 0.936 9.

prostate cancerGleason grade groups(GGs)bi-parametric magnetic resonance imagingdeep learn-ingcurriculum learning

沈傲、胡冀苏、金鹏飞、周志勇、钱旭升、郑毅、包婕、王希明、戴亚康

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School of Computer Science and Technology,Changchun University of Science and Technology,Changchun 130022,China

Suzhou Institute of Biomedical Engineering and Technology,Chinese Academy of Sciences,Suzhou 215163,Jiangsu,China

School of Biomedical Engineering(SooChow),University of Science and Technology of China,Suzhou 215163,Jiangsu,China

Department of Radiology,The First Affiliated Hospital of Soochow University,Suzhou 215006,Jiangsu,China

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Suzhou Municipal Health and Family Planning Commission's Key Diseases Diagnosis and Treatment ProgramScience and Technology Development Project of SuzhouScience and Technology Development Project of Suzhou中国科学院青年创新促进会项目Medical Research Project of Jiangsu Provincial Health and Family Planning Commission

LCZX202001SS2019012SKY20210312021324M2020068

2024

10.1007/s12204-022-2502-x

上海交通大学学报(英文版)
上海交通大学

上海交通大学学报(英文版)

影响因子：0.151
ISSN：1007-1172
年,卷(期)：2024.29(1)
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