  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.
