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：疾病进展模拟是一个关键的研究领域，对临床诊断、预后和治疗具有重要意义。该领域面临的主要挑战之一是缺乏对个体患者进行长期连续医学影像监测。为解决这一问题，我们开发了一个名为渐进式图像编辑（PIE）的新型框架，该框架能够实现疾病相关图像特征的受控操作，从而促进精确且逼真的疾病进展模拟。具体而言，我们利用文本到图像生成模型的最新进展，准确模拟疾病进展并实现针对每位患者的个性化定制。我们从理论上将框架中的迭代优化过程分析为一种具有指数衰减学习率的梯度下降。为验证该框架，我们在三个医学影像领域进行了实验。结果表明，PIE在基于CLIP分数（逼真度）和疾病分类置信度（对齐性）的现有方法（如Stable Diffusion Walk和基于流形的风格外推法）中表现出优越性。我们通过用户研究收集了35位资深医生的反馈，以评估生成的进展图像。值得注意的是，76.2%的反馈认可了生成进展图像的真实性。据我们所知，PIE是首个能够生成符合实际标准的疾病进展图像的方法。它有望成为医学研究和临床实践中的有力工具，助力医疗提供者模拟疾病随时间变化的轨迹、预测未来治疗反应并改善患者预后。