An explainable three dimension framework to uncover learning patterns: A unified look in variable sulci recognition
arxiv(2023)
摘要
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.
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