一种可解释的三维框架以揭示学习模式：在可变脑沟识别中的统一视角
An explainable three dimension framework to uncover learning patterns: A unified look in variable sulci recognition
AI总结：
本文提出一种可解释AI的3D框架，结合Shape、GradCam和SHAP方法与降维技术，在596个结构MRI上检测扣带旁沟，提供准确低复杂度的全局解释，揭示了关键脑区及大脑发育规律。
摘要：
在人工智能模型的学习过程中，从数据集的代表性子集中识别出的重要特征被称为“全局”解释。3D全局解释在神经影像学中至关重要，因为复杂的表征空间需要超越基础的2D解释。然而，当前文献中的研究往往缺乏神经影像学及其他领域所需的准确性、可理解性和3D全局解释。为了解决这一空白，我们开发了一个可解释人工智能（XAI）3D框架，能够提供准确、低复杂度的全局解释。我们使用在596个结构MRI的标注良好队列上训练的各种3D深度学习模型评估了该框架。二分类任务专注于检测扣带旁沟的存在或缺失，这是一种与精神病高度相关的可变大脑结构。我们的框架将统计特征（Shape）和XAI方法（GradCam和SHAP）与降维相结合，确保解释既反映模型学习又反映队列特定的变异性。通过结合Shape、GradCam和SHAP，我们的框架减少了方法间的变异性，增强了全局解释的忠实度和可靠性。这些稳健的解释促进了关键子区域的识别，包括后颞叶和内顶叶区域，以及扣带区和丘脑，表明了潜在的遗传或发育影响。我们的XAI 3D框架利用全局解释来揭示特定皮质特征的更广泛发育背景。该方法通过提供对正常大脑发育和与精神疾病相关的非典型轨迹的洞察，推进了深度学习和神经科学领域，为神经影像学中更可靠和可解释的AI应用铺平了道路。
英文摘要：
The significant features identified in a representative subset of the dataset during the learning process of an artificial intelligence model are referred to as a 'global' explanation. 3D global explanations are crucial in neuroimaging, where a complex representational space demands more than basic 2D interpretations. However, current studies in the literature often lack the accuracy, comprehensibility, and 3D global explanations needed in neuroimaging and beyond. To address this gap, we developed an explainable artificial intelligence (XAI) 3D-Framework capable of providing accurate, low-complexity global explanations. We evaluated the framework using various 3D deep learning models trained on a well-annotated cohort of 596 structural MRIs. The binary classification task focused on detecting the presence or absence of the paracingulate sulcus, a highly variable brain structure associated with psychosis. Our framework integrates statistical features (Shape) and XAI methods (GradCam and SHAP) with dimensionality reduction, ensuring that explanations reflect both model learning and cohort-specific variability. By combining Shape, GradCam, and SHAP, our framework reduces inter-method variability, enhancing the faithfulness and reliability of global explanations. These robust explanations facilitated the identification of critical sub-regions, including the posterior temporal and internal parietal regions, as well as the cingulate region and thalamus, suggesting potential genetic or developmental influences. Our XAI 3D-Framework leverages global explanations to uncover the broader developmental context of specific cortical features. This approach advances the fields of deep learning and neuroscience by offering insights into normative brain development and atypical trajectories linked to mental illness, paving the way for more reliable and interpretable AI applications in neuroimaging.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
