Disease progression simulation is a crucial area of research that has significant implications for clinical diagnosis, prognosis, and treatment. One major challenge in this field is the lack of continuous medical imaging monitoring of individual patients over time. To address this issue, we develop a novel framework termed Progressive Image Editing (PIE) that enables controlled manipulation of disease-related image features, facilitating precise and realistic disease progression simulation. Specifically, we leverage recent advancements in text-to-image generative models to simulate disease progression accurately and personalize it for each patient. We theoretically analyze the iterative refining process in our framework as a gradient descent with an exponentially decayed learning rate. To validate our framework, we conduct experiments in three medical imaging domains. Our results demonstrate the superiority of PIE over existing methods such as Stable Diffusion Walk and Style-Based Manifold Extrapolation based on CLIP score (Realism) and Disease Classification Confidence (Alignment). Our user study collected feedback from 35 veteran physicians to assess the generated progressions. Remarkably, 76.2% of the feedback agrees with the fidelity of the generated progressions. To our best knowledge, PIE is the first of its kind to generate disease progression images meeting real-world standards. It is a promising tool for medical research and clinical practice, potentially allowing healthcare providers to model disease trajectories over time, predict future treatment responses, and improve patient outcomes.

翻译：摘要：疾病进展模拟是临床诊断、预后及治疗领域具有重要影响的研究方向。该领域的一大挑战在于缺乏对个体患者随时间推移的连续医学影像监测。为解决该问题，我们提出名为渐进式图像编辑（PIE）的创新框架，能够实现疾病相关图像特征的可控操作，从而促进精确且逼真的疾病进展模拟。具体而言，我们利用最新文本到图像生成模型的进展，以精确模拟疾病进展并针对每位患者进行个性化调整。我们从理论上将框架中的迭代优化过程分析为具有指数衰减学习率的梯度下降方法。为验证框架有效性，我们在三个医学影像领域开展实验。结果表明，PIE在基于CLIP评分（真实性）和疾病分类置信度（一致性）方面，优于Stable Diffusion Walk和基于风格的流形外推等现有方法。我们通过用户研究收集了35位资深医师的反馈以评估生成的进展序列，其中76.2%的反馈认可生成进展的真实性。据我们所知，PIE是首个能生成符合真实世界标准的疾病进展影像的方法。该工具对医学研究与临床实践具有重要应用前景，可助力医疗人员建模疾病随时间演变的轨迹、预测未来治疗反应并改善患者预后。