PIE: Simulating Disease Progression via Progressive Image Editing

TL;DR

This paper introduces PIE, a novel framework that uses progressive image editing with generative models to simulate disease progression in medical images, aiding clinical diagnosis and treatment planning.

Contribution

PIE is the first framework to enable realistic, personalized disease progression simulation through controlled image editing using advanced generative models.

Findings

PIE outperforms existing methods in realism and disease classification confidence.

Physicians agree 76.2% of the time on the fidelity of generated progressions.

Theoretical analysis links the iterative refinement to gradient descent with decayed learning rate.

Abstract

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.