AI models have shown promise in many medical imaging tasks. However, our ability to explain what signals these models have learned is severely lacking. Explanations are needed in order to increase the trust in AI-based models, and could enable novel scientific discovery by uncovering signals in the data that are not yet known to experts. In this paper, we present a method for automatic visual explanations leveraging team-based expertise by generating hypotheses of what visual signals in the images are correlated with the task. We propose the following 4 steps: (i) Train a classifier to perform a given task (ii) Train a classifier guided StyleGAN-based image generator (StylEx) (iii) Automatically detect and visualize the top visual attributes that the classifier is sensitive towards (iv) Formulate hypotheses for the underlying mechanisms, to stimulate future research. Specifically, we present the discovered attributes to an interdisciplinary panel of experts so that hypotheses can account for social and structural determinants of health. We demonstrate results on eight prediction tasks across three medical imaging modalities: retinal fundus photographs, external eye photographs, and chest radiographs. We showcase examples of attributes that capture clinically known features, confounders that arise from factors beyond physiological mechanisms, and reveal a number of physiologically plausible novel attributes. Our approach has the potential to enable researchers to better understand, improve their assessment, and extract new knowledge from AI-based models. Importantly, we highlight that attributes generated by our framework can capture phenomena beyond physiology or pathophysiology, reflecting the real world nature of healthcare delivery and socio-cultural factors. Finally, we intend to release code to enable researchers to train their own StylEx models and analyze their predictive tasks.

翻译：人工智能模型在多项医学影像任务中展现出潜力，但当前我们严重缺乏对这些模型所学信号的解释能力。为提升对基于人工智能的模型的信任度，并揭示数据中尚未被专家认知的信号以实现新型科学发现，解释机制不可或缺。本文提出一种基于团队专业知识的自动视觉解释方法，通过生成图像中与任务相关的视觉信号假设来实现目标。具体包含以下四个步骤：（i）训练分类器以完成特定任务；（ii）训练由分类器引导的基于StyleGAN的图像生成模型（StylEx）；（iii）自动检测并可视化分类器最敏感的顶级视觉属性；（iv）构建潜在机制的假设以推动未来研究。我们将所发现的属性呈现给跨学科专家小组，使假设能够涵盖健康的社会与结构性决定因素。我们在三种医学影像模态的八项预测任务中验证了方法：视网膜眼底照片、外部眼部照片及胸部X光片。示例显示，所提取属性既能捕获临床已知特征，又能揭示超越生理机制的混杂因素，还呈现了多项生理学上合理的未知属性。该方法有望帮助研究者更深入理解AI模型、提升评估能力并从中获取新知识。重要的是，我们强调框架生成的属性可捕捉超出生理学或病理生理学范畴的现象，反映医疗实践的现实特征及社会文化因素。最后，我们将开源代码以支持研究者训练自有StylEx模型并分析其预测任务。