Frontotemporal Dementia (FTD) diagnosis has been successfully progress using deep learning techniques. However, current FTD identification methods suffer from two limitations. Firstly, they do not exploit the potential of multi-view functional magnetic resonance imaging (fMRI) for classifying FTD. Secondly, they do not consider the reliability of the multi-view FTD diagnosis. To address these limitations, we propose a reliable multi-view impartial decision network (MID-Net) for FTD diagnosis in fMRI. Our MID-Net provides confidence for each view and generates a reliable prediction without any conflict. To achieve this, we employ multiple expert models to extract evidence from the abundant neural network information contained in fMRI images. We then introduce the Dirichlet Distribution to characterize the expert class probability distribution from an evidence level. Additionally, a novel Impartial Decision Maker (IDer) is proposed to combine the different opinions inductively to arrive at an unbiased prediction without additional computation cost. Overall, our MID-Net dynamically integrates the decisions of different experts on FTD disease, especially when dealing with multi-view high-conflict cases. Extensive experiments on a high-quality FTD fMRI dataset demonstrate that our model outperforms previous methods and provides high uncertainty for hard-to-classify examples. We believe that our approach represents a significant step toward the deployment of reliable FTD decision-making under multi-expert conditions. We will release the codes for reproduction after acceptance.

翻译：额颞叶痴呆（FTD）诊断借助深度学习技术已取得显著进展。然而，现有FTD识别方法存在两个局限：其一，未能充分利用多视角功能磁共振成像（fMRI）进行FTD分类的潜力；其二，未考虑多视角FTD诊断的可靠性。为解决这些问题，我们提出了一种可靠的基于fMRI的FTD诊断多视角无偏决策网络（MID-Net）。该网络可为每个视角提供置信度，并在无冲突条件下生成可靠预测。具体而言，我们采用多个专家模型从fMRI图像蕴含的丰富神经网络信息中提取证据，随后引入狄利克雷分布（Dirichlet Distribution）从证据层面表征专家类概率分布。此外，我们创新性地提出无偏决策器（IDer），通过归纳融合不同专家意见，在不增加额外计算成本的前提下获得无偏预测。整体而言，我们的MID-Net可动态整合不同专家对FTD疾病的决策，尤其在处理多视角高度冲突案例时表现优异。在高质量FTD fMRI数据集上的大量实验表明，该模型性能优于现有方法，并能对难以分类的样本提供高不确定性评估。我们相信，该方法向多专家条件下可靠FTD决策系统的部署迈出了重要一步。论文接收后我们将开放代码以供复现。