Explainable AI is crucial in medical imaging. In the challenging field of neuroscience, visual topics present a high level of complexity, particularly within three-dimensional space. The application of neuroscience, which involves identifying brain sulcal features from MRI, faces significant hurdles due to varying annotation protocols among experts and the intricate three-dimension functionality of the brain. Consequently, traditional explainability approaches fall short in effectively validating and evaluating these networks. To address this, we first present a mathematical formulation delineating various categories of explanation needs across diverse computer vision tasks, categorized into self-explanatory, semi-explanatory, non-explanatory, and new-pattern learning applications based on the reliability of the validation protocol. With respect to this mathematical formulation, we propose a 3D explainability framework aimed at validating the outputs of deep learning networks in detecting the paracingulate sulcus an essential brain anatomical feature. The framework integrates local 3D explanations, global explanations through dimensionality reduction, concatenated global explanations, and statistical shape features, unveiling new insights into pattern learning. We trained and tested two advanced 3D deep learning networks on the challenging TOP-OSLO dataset, significantly improving sulcus detection accuracy, particularly on the left hemisphere. During evaluation with diverse annotation protocols for this dataset, we highlighted the crucial role of an unbiased annotation process in achieving precise predictions and effective pattern learning within our proposed 3D framework. The proposed framework not only annotates the variable sulcus but also uncovers hidden AI knowledge, promising to advance our understanding of brain anatomy and function.

翻译：可解释人工智能在医学影像中至关重要。在神经科学这一充满挑战的领域，视觉主题具有高度复杂性，尤其体现在三维空间中。由于专家间标注协议存在差异以及大脑复杂的三维功能，神经科学应用（如基于MRI识别脑沟特征）面临显著障碍。因此，传统可解释性方法在有效验证和评估这些网络方面存在不足。为解决此问题，我们首先提出一个数学公式，将不同计算机视觉任务中的解释需求分为自解释、半解释、非解释和新模式学习应用四类，分类依据是验证协议的可靠性。基于该数学公式，我们提出一个三维可解释性框架，旨在验证深度学习网络在检测前扣带沟（一种关键的大脑解剖特征）时的输出。该框架整合了局部三维解释、通过降维实现的全局解释、拼接式全局解释以及统计形状特征，揭示了模式学习的新见解。我们在具有挑战性的TOP-OSLO数据集上训练并测试了两个先进的三维深度学习网络，显著提高了脑沟检测的准确性，特别是在左半球。在针对该数据集采用不同标注协议进行评估时，我们强调了无偏标注过程对我们提出的三维框架中实现精确预测和有效模式学习的关键作用。该框架不仅能标注可变脑沟，还能揭示隐藏的AI知识，有望推动我们对大脑解剖结构和功能的理解。