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Deep Multi-Modal Classification of Intraductal Papillary Mucinous Neoplasms (IPMN) with Canonical Correlation Analysis

机译:导管内乳头状粘液纤维的深层多模态分类   具有典型相关分析的肿瘤(IpmN)

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摘要

Pancreatic cancer has the poorest prognosis among all cancer types.Intraductal Papillary Mucinous Neoplasms (IPMNs) are radiographicallyidentifiable precursors to pancreatic cancer; hence, early detection andprecise risk assessment of IPMN are vital. In this work, we propose aConvolutional Neural Network (CNN) based computer aided diagnosis (CAD) systemto perform IPMN diagnosis and risk assessment by utilizing multi-modal MRI. Inour proposed approach, we use minimum and maximum intensity projections to easethe annotation variations among different slices and type of MRIs. Then, wepresent a CNN to obtain deep feature representation corresponding to each MRImodality (T1-weighted and T2-weighted). At the final step, we employ canonicalcorrelation analysis (CCA) to perform a fusion operation at the feature level,leading to discriminative canonical correlation features. Extracted featuresare used for classification. Our results indicate significant improvements overother potential approaches to solve this important problem. The proposedapproach doesn't require explicit sample balancing in cases of imbalancebetween positive and negative examples. To the best of our knowledge, our studyis the first to automatically diagnose IPMN using multi-modal MRI.
机译:在所有类型的癌症中,胰腺癌的预后最差。导管内乳头状黏液性肿瘤(IPMN)是影像学上可识别的胰腺癌前体;因此,IPMN的早期检测和精确的风险评估至关重要。在这项工作中,我们提出了一个基于卷积神经网络(CNN)的计算机辅助诊断(CAD)系统,以利用多模式MRI进行IPMN诊断和风险评估。在我们提出的方法中,我们使用最小和最大强度投影来缓解不同切片和MRI类型之间的注释差异。然后,我们表示一个CNN,以获得与每个MRI模态(T1加权和T2加权)相对应的深层特征表示。在最后一步,我们使用规范相关分析(CCA)在特征级别执行融合操作,从而产生可区分的规范相关特征。提取的特征用于分类。我们的结果表明,与解决该重要问题的其他潜在方法相比,已有显着改进。在正例和负例之间不平衡的情况下,建议的方法不需要显式的样本平衡。据我们所知,我们的研究是第一个使用多模式MRI自动诊断IPMN的研究。

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