贾熹滨, 孙政, 杨大为, 杨正汉. 自注意力指导的多序列融合肝细胞癌分化判别模型[J]. 工程科学学报, 2021, 43(9): 1149-1156. DOI: 10.13374/j.issn2095-9389.2021.01.13.003
引用本文: 贾熹滨, 孙政, 杨大为, 杨正汉. 自注意力指导的多序列融合肝细胞癌分化判别模型[J]. 工程科学学报, 2021, 43(9): 1149-1156. DOI: 10.13374/j.issn2095-9389.2021.01.13.003
JIA Xi-bin, SUN Zheng, YANG Da-wei, YANG Zheng-han. Self-attention guided multi-sequence fusion model for differentiation of hepatocellular carcinoma[J]. Chinese Journal of Engineering, 2021, 43(9): 1149-1156. DOI: 10.13374/j.issn2095-9389.2021.01.13.003
Citation: JIA Xi-bin, SUN Zheng, YANG Da-wei, YANG Zheng-han. Self-attention guided multi-sequence fusion model for differentiation of hepatocellular carcinoma[J]. Chinese Journal of Engineering, 2021, 43(9): 1149-1156. DOI: 10.13374/j.issn2095-9389.2021.01.13.003

自注意力指导的多序列融合肝细胞癌分化判别模型

Self-attention guided multi-sequence fusion model for differentiation of hepatocellular carcinoma

  • 摘要: 结合影像学和人工智能技术对病灶进行无创性定量分析是目前智慧医疗的一个重要研究方向。针对肝细胞癌(Hepatocellular carcinoma, HCC)分化程度的无创性定量估测方法研究,结合放射科医师的临床读片经验,提出了一种基于自注意力指导的多序列融合肝细胞癌组织学分化程度无创判别计算模型。以动态对比增强核磁共振成像(Dynamic contrast-enhanced magnetic resonance imaging, DCE-MRI)的多个序列为输入,学习各时序序列及各序列的多层扫描切片在分化程度判别任务的权重,加权序列中具有的良好判别性能的时间和空间特征,以提升分化程度判别性能。模型的训练和测试在三甲医院的临床数据集上进行,实验结果表明,本文所提出的肝细胞癌分化程度判别模型取得相比几种基准和主流模型最高的分类计算性能,在WHO组织学分级任务中,判别准确度、灵敏度、精确度分别达到80%,82%和82%。

     

    Abstract: Hepatocellular carcinoma (HCC) is a type of primary malignant tumor and an urgent problem to be solved, particularly in China, one of the countries with the highest prevalence of HCC. In the choice of treatment methods for patients with hepatocellular carcinoma, accurate histological grading of the lesion area undoubtedly plays a vital role that helps the management and therapy of HCC patients. However, the current pathological detection as the gold standard has defects, such as invasiveness and a large sampling error. Therefore, it is an important direction of intelligent medical treatment to provide noninvasive and accurate lesion grading using imaging technology combined with artificial intelligence technology. On the basis of the radiologists' experience in reading clinical images, this paper proposed a self-attentional guidance-based histological differentiation discrimination model combined with multi-modality fusion and an attention weight calculation scheme for dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) sequences of hepatocellular carcinoma. The model combined the spatiotemporal information contained in the enhancement sequence and learned the importance of each sequence and the slice in the sequence for the classification task. It effectively used the feature information contained in the enhancement sequence in the temporal and spatial dimensions to improve the classification performance. During the experiment, the model was trained and tested on the clinical data set of the top three hospitals in China. The experimental results show that the self-attention-guided model proposed in this paper achieves higher classification performance than several benchmark and mainstream models. Comprehensive experiments were performed on the clinical dataset with labels annotated by professional radiologists. The results show that our proposed self-attention model can achieve acceptable quantitative measuring of HCC histologic grading based on the MRI sequences. In the WHO histological classification task, the classification accuracy of the proposed model reaches 80%, the sensitivity is 82%, and the precision is 82%.

     

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