This study employs counterfactual explanations to explore "what if?"
scenarios in medical research, with the aim of expanding our understanding
beyond existing boundaries. Specifically, we focus on utilizing MRI features
for diagnosing pediatric posterior fossa brain tumors as a case study. The
field of artificial intelligence and explainability has witnessed a growing
number of studies and increasing scholarly interest. However, the lack of
human-friendly interpretations in explaining the outcomes of machine learning
algorithms has significantly hindered the acceptance of these methods by
clinicians in their clinical practice. To address this, our approach
incorporates counterfactual explanations, providing a novel way to examine
alternative decision-making scenarios. These explanations offer personalized
and context-specific insights, enabling the validation of predictions and
clarification of variations under diverse circumstances. Importantly, our
approach maintains both statistical and clinical fidelity, allowing for the
examination of distinct tumor features through alternative realities.
Additionally, we explore the potential use of counterfactuals for data
augmentation and evaluate their feasibility as an alternative approach in
medical research. The results demonstrate the promising potential of
counterfactual explanations to enhance trust and acceptance of AI-driven
methods in clinical settings.

该研究采用反事实解释方法探索医学研究的 “假设场景”，特别关注利用 MRI 特征诊断儿科后颅窝脑肿瘤，并探索反事实的潜在用途并评估其作为医疗研究中的替代方法的可行性和前景。