In response to the global need for efficient early diagnosis of Autism Spectrum Disorder (ASD), this paper bridges the gap between traditional, time-consuming diagnostic methods and potential automated solutions. We propose a multi-atlas deep ensemble network, MADE-for-ASD, that integrates multiple atlases of the brain's functional magnetic resonance imaging (fMRI) data through a weighted deep ensemble network. Our approach integrates demographic information into the prediction workflow, which enhances ASD diagnosis performance and offers a more holistic perspective on patient profiling. We experiment with the well-known publicly available ABIDE (Autism Brain Imaging Data Exchange) I dataset, consisting of resting state fMRI data from 17 different laboratories around the globe. Our proposed system achieves 75.20% accuracy on the entire dataset and 96.40% on a specific subset $-$ both surpassing reported ASD diagnosis accuracy in ABIDE I fMRI studies. Specifically, our model improves by 4.4 percentage points over prior works on the same amount of data. The model exhibits a sensitivity of 82.90% and a specificity of 69.70% on the entire dataset, and 91.00% and 99.50%, respectively, on the specific subset. We leverage the F-score to pinpoint the top 10 ROI in ASD diagnosis, such as \emph{precuneus} and anterior \emph{cingulate/ventromedial}. The proposed system can potentially pave the way for more cost-effective, efficient and scalable strategies in ASD diagnosis. Codes and evaluations are publicly available at TBA.

翻译：为应对全球对自闭症谱系障碍高效早期诊断的需求，本文弥合了传统耗时诊断方法与潜在自动化解决方案之间的差距。我们提出了一种多图谱深度集成网络——MADE-for-ASD，该网络通过加权深度集成网络整合了大脑功能磁共振成像数据的多个图谱。我们的方法将人口统计学信息整合到预测工作流程中，从而提升了ASD诊断性能，并为患者画像提供了更全面的视角。我们在著名的公开数据集ABIDE I上进行了实验，该数据集包含来自全球17个不同实验室的静息态fMRI数据。我们提出的系统在整个数据集上达到了75.20%的准确率，在特定子集上达到了96.40%的准确率——两者均超过了ABIDE I fMRI研究中已报道的ASD诊断准确率。具体而言，在相同数据量上，我们的模型比先前工作提升了4.4个百分点。该模型在整个数据集上的敏感度为82.90%，特异度为69.70%；在特定子集上则分别为91.00%和99.50%。我们利用F分数确定了ASD诊断中排名前10的关键脑区，例如楔前叶和前扣带回/腹内侧前额叶。所提出的系统有望为ASD诊断中更具成本效益、更高效和可扩展的策略铺平道路。代码和评估结果将在TBA公开提供。