基于CT图像的双重注意力网络急性胰腺炎诊断方法  

Dual-Attention Network for Acute Pancreatitis Diagnosis with CT Images

作　　者：张进一 万鹏[1] 孙亮 张道强[1] ZHANG Jinyi;WAN Peng;SUN Liang;ZHANG Daoqiang(College of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,MIIT Key Laboratory of Pattern Analysis and Machine Intelligence,Nanjing 211106,China)

机构地区：[1]南京航空航天大学计算机科学与技术学院,模式分析与机器智能工业和信息化部重点实验室,南京211106

出　　处：《数据采集与处理》2022年第1期147-154,共8页Journal of Data Acquisition and Processing

基　　金：国家自然科学基金(61876082,61861130366,61732006);国家重点研发计划(2018YFC2001600,2018YFC2001602)。

摘　　要：作为消化系统最常见的疾病之一,急性胰腺炎的医学影像仍使用简单的手工特征进行分析,效率与精度较低,与其危害性并不相称。由于胰腺的解剖变异性以及各种并发症,急性胰腺炎的影像表现复杂,不同患者不同种类的病灶差异大,基于CT影像的急性胰腺炎诊断难度较大。本文提出一种基于双重注意力网络用于诊断急性胰腺炎,该网络使用全局特征为不同阶段的局部特征生成局部注意力特征,使不同阶段的注意力特征关注不同尺度的病灶,最终通过融合对全局注意力特征进行分类。同时在生成注意力特征时,使用通道域注意力调整通道间的依赖,提高模型的表示能力。在真实的急性胰腺炎数据集上的实验结果表明,本文提出的网络取得了更好的急性胰腺炎诊断精度,相对原模型,灵敏度与曲线下面积(Area under the curve,AUC)分别至少提升了3.4%,3.2%;相较其他注意力机制如SENet对ResNet模型的改进,AUC提升2.7%。Acute pancreatitis(AP)is one of the most common digestive disease,while the analysis based on medical images of AP still depends on simple manual features with low efficiency and accuracy,which is not commensurate with AP’s harmfulness.Due to the anatomical variation of pancreas and complications of AP,AP has complex imaging manifestations and large appearance pattern variation of lesions that exist among patients and lesion kinds.It is challenging for diagnosis of acute pancreatitis based on CT images.To address these issues,we propose a dual-attention network for acute pancreatitis diagnosis.Specifically,the dual-attention network utilizes the global feature to generate local attention feature for each local feature on different stages,and final classification is facilitated by the fusion of multi-scale attention features focusing on lesions of different scales.Meanwhile,channel-domain attention is used to produce attention features based on the dependencies between each channel to improve the model’s feature representation ability.We evaluate the proposed method on the collected real acute pancreatitis dataset.Results show that the proposed network achieve superior performance in acute pancreatitis diagnosis compared with several competing methods,with the sensitivity improved by 3.4%.And the improvement of area under the curve(AUC)the proposed network brings to ResNet is 2.7%higher than other attention model such as SENet.

关 键 词：急性胰腺炎 双重注意力 多尺度 CT 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]