目的 随着全球人口老年化趋势日益加剧,阿尔茨海默症（Alzheimer's disease，AD）的及时诊断与病理区域的可视化及其准确定位具有重要的临床意义。目前的研究中,基于块级和区域级的检测,由于采用非线性交互很难解释影响模型决策的病理区域。针对此问题,提出了一种AD病理区域定位及诊断的联合学习框架。方法 利用反事实推理的思想,基于前景背景注意力掩码构建注意力引导的循环生成对抗网络（attention-guided cycle generative adversarial network，ACGAN）可视化AD患者的病理区域,并使用生成的病理区域知识指导增强诊断模型。具体来说,通过在ACGAN模型的生成器中设计注意力掩码来引导生成方案,使模型更好地聚焦于疾病的病理区域,有效地捕捉突出的全局特征。并通过ACGAN模型中病理区域生成器实现结构磁共振图像（structural magnetic resonance imaging，sMRI）在源域和目标域之间的转换清晰地划分出细微的病理区域。利用生成的病理区域知识作为指导,并结合三维坐标注意力与全局局部注意力,获取三维图像之间的依赖关系及三维空间的位置信息,优化诊断模型。结果 为了验证方法的有效性,在公开的ADNI（Alzheimer’s disease neuroimaging initiative）数据集上对模型进行评估,与传统的卷积神经网络（convolutional neural network,CNN）模型及几种较为先进的AD分类诊断模型相比,本文使用病理区域知识指导增强诊断模型显示出优越的诊断性能,相比于性能较好的方法,ACC（accuracy）、F1-score、AUC（area under curve）分别提高了3.60%、5.02%、1.94%。并对生成的病理区域图像进行定性及定量评估,本文方法得到的病理区域图像归一化互相关分数和峰值信噪比均优于对比方法。结论 与现有方法相比,本文模型可以学习sMRI图像在源域和目标域之间的转换,能够准确地捕获全局特征及病理区域。并将学习到的病理区域知识用于AD诊断模型的改进,使分类诊断模型取得了卓越性能。

Objective Alzheimer’s disease（AD）is a neurodegenerative disease commonly occurring in middle-aged and elderly populations and is accompanied by cognitive impairment and memory loss. With the increasing trend of global population aging,timely diagnosis of AD and visualization of pathological regions and its accurate localization are of considerable clinical importance. In current research,one conventional approach is to extract patch-level features based on voxel morphology and prior knowledge for detecting structural changes and identifying AD-related voxel structures. Another approach is to learn AD-related pathological regions by focusing the network on specific brain regions of interest（e.g.,cortical and hippocampal regions）based on regional features. However,these approaches ignore other pathological locations in the brain and fail to obtain accurate global structural information for the diagnosis of AD. A joint learning model for localization and diagnosis of AD pathological regions is proposed using the idea of counterfactual reasoning to obtain a convincing model architecture and increase interpretability of the output,highlighting the information of pathological regions. An attention-guided cycle generative adversarial network（ACGAN）is constructed based on foreground-background attention mask. Method In the vast majority of image classification methods,the network model aims to find which part of the input is X and which part influences the decision of the classifier to determine the final result as Y. From another viewpoint,in a hypothetical scenario where the input X is C,would the result be Z instead of Y？ This idea is defined as counterfactual reasoning. The AD classification model was first trained as a classifier in the hypothetical scenario to construct its output,and the pathological features of AD were then obtained. The hypothetical scenario was constructed using a generative adversarial network to learn the mapping of images from the source domain to the target field. However,achieving good results by directly generating image to image transformation is difficult due to the complexity of whole brain structural magnetic resonance imaging（sMRI）images and the considerable amount of information in 3D space. Drawing inspiration from two models,namely CycleGAN and AttentionGAN,the image can be mapped from the source to the target domain by changing the region in the original image that affects the category judgment and using the foreground-background attention to guide the model to focus on the dynamic change region,which reduces the complexity of the model and facilitates easy model f桩整?扩敮湧挮栠浔慨牥歲?浦敯瑲桥漬摴???戠??潰湥捲氠異獲楯潰湯??扳???漠浡灴慴牥敮摴?睯楮琭桧?敩硤楥獤琠楲湥杣?浲敲瑥桮潴搠獧?瑮桥楲獡?????乡?浶潥摲敳污?捩慡湬?汮敥慴牷湯?瑫栠整?琠牣慯湮獳晴潲牵浣慴琠楡漠湣?潵普?獥?剦??楴浵慡杬攠獭?扰数瑩睮敧攠湭?獤潥畬爠捦敯?愠湁摄?瑴慨牥杲敥瑢?映楯敵汴摰獵?慴湩摮?愠捴捨略爠慣瑯敲汲祥?捰慯灮瑤畩牮敧?杰污潴扨慯汬?晧敩慣瑡畬爠敲獥?慩湯摮?瀮愠瑉桦漠污漠杣楯捵慮汴?牲敦条楣潴湵獡??呭桡数?汣敯慮牤湩整摩?歮湡潬眠汯敮搠杴敨?漠晴?瑲桧敥?瀠慬瑡桢潥汬漈杩椮捥愮氬?特数杯楴潨湥?楩獣?畬猠敳摣?普潡牲?瑯栉敩?椠浧灥牮潥癲敡浴敥湤琬?潨晥????摩楳愠杣湯潵獮楴獥?浦潡摣整汵獡??呭桡数爠敩晳漠牡敤?瑥桤攠?捯氠慴獨獥椠晩楮捰慵瑴椠潩湭?摧楥愠杴湯漠獤楩獡?浮潯摳敥氠?慨捥栠楴敲癡敮獳?敯硲捭敥汤氠敩湭瑡?灥攠牡晳漠牴浨慥渠捴敡?get type. For example,when the counterfactual map is added to the sMRI image of a subject with AD,modifying the corresponding region changes the input sMRI image and facilitates the diagnosis by the classifier as a normal subject. The pathological regions represented by the counterfactual map were used as privileged information（i.e.,the location information of the counterfactual map influenced the category determination）to further optimize the diagnostic model. Therefore,the diagnostic model focused on learning and discovering disease-related discriminative regions to combine the pathological region generation and AD diagnostic models. Result The proposed model was evaluated against traditional convolutional neural network （CNN）models and several highly advanced AD diagnostic models on a publicly available ADNI dataset using quantitative evaluation metrics,including accuracy（ACC）,F1-score,and area under curve （AUC）. Experimental results showed that the model improved ACC,F1-score,and AUC by 3. 60%,5. 02%,and 1.94%,respectively,compared with the best performing method. The generated pathological region images were also qualitatively and quantitatively evaluated,and the normalized correlation scores and peak signal-to-noise ratios of the pathological region images obtained by the method were better than those of the compared methods. More importantly,the proposed AD diagnostic model visualized the global features and finegrained discriminative regions of the pathological regions compared with the benchmark model,and the average accuracy after three iterations was improved by +4.90%,+11.03%, and +11.08% compared with t