  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.
