In the field of medical imaging, particularly in tasks related to early
disease detection and prognosis, understanding the reasoning behind AI model
predictions is imperative for assessing their reliability. Conventional
explanation methods encounter challenges in identifying decisive features in
medical image classifications, especially when discriminative features are
subtle or not immediately evident. To address this limitation, we propose an
agent model capable of generating counterfactual images that prompt different
decisions when plugged into a black box model. By employing this agent model,
we can uncover influential image patterns that impact the black model's final
predictions. Through our methodology, we efficiently identify features that
influence decisions of the deep black box. We validated our approach in the
rigorous domain of medical prognosis tasks, showcasing its efficacy and
potential to enhance the reliability of deep learning models in medical image
classification compared to existing interpretation methods. The code will be
publicly available at this https URL

通过生成对抗图像，我们提出了一种方法来揭示影响黑盒模型的决策的关键特征，以提高医学图像分类中深度学习模型的可靠性。