In medical imaging, particularly in early disease detection and prognosis tasks, discerning the rationale behind an AI model's predictions is crucial for evaluating the reliability of its decisions. Conventional explanation methods face challenges in identifying discernible decisive features in medical image classifications, where discriminative features are subtle or not immediately apparent. To bridge this gap, we propose an explainable model that is equipped with both decision reasoning and feature identification capabilities. Our approach not only detects influential image patterns but also uncovers the decisive features that drive the model's final predictions. By implementing our method, we can efficiently identify and visualise class-specific features leveraged by the data-driven model, providing insights into the decision-making processes of deep learning models. We validated our model in the demanding realm of medical prognosis task, demonstrating its efficacy and potential in enhancing the reliability of AI in healthcare and in discovering new knowledge in diseases where prognostic understanding is limited.

在医学成像中，辨识人工智能模型预测背后的理由对于评估其决策的可靠性至关重要。为了填补这一差距，我们提出了一种具备决策推理和特征识别能力的可解释模型，不仅能够检测有影响力的图像模式，还能揭示驱动模型最终预测的决定性特征。通过实施我们的方法，我们能够有效地识别和可视化数据驱动模型利用的类别特定特征，为深度学习模型的决策过程提供洞察，验证了我们的模型在医学预测任务中的效用和潜力，从而提高了人工智能在医疗保健领域的可靠性，并发现了在预后理解有限的疾病中的新知识。

决策推理解码：基于反事实的模型在知识发现中的运用