Reproduce “DepthPilot: From Controllability to Interpretability in Colonoscopy Video Generation” on OpenResearch

About this paper

Junhu Fu, Ke Chen, Weidong Guo, Shuyu Liang, Jie Xu, Chen Ma, Kehao Wang, Shengli Lin and 4 more

DepthPilot introduces a diffusion-based framework for generating clinically interpretable colonoscopy videos by integrating geometric grounding via depth priors and an adaptive spline denoising module. This framework generates anatomically faithful colonoscopy videos, achieving superior realism and clinical interpretability compared to existing methods.

Key insights

• Explicitly guiding video generation with physical priors, such as depth sequences, is crucial for achieving clinical interpretability and ensuring generated content adheres to anatomical and physiological constraints.
• Enhancing the nonlinear modeling capability of diffusion backbones through adaptive, learnable activation functions (parameterized by B-splines) is effective for capturing the complex spatio-temporal dynamics of medical procedures like colonoscopy.
• A targeted, two-stage fine-tuning approach, freezing most parameters while adapting specialized modules, efficiently integrates new constraints without catastrophic forgetting, balancing broad generative power with specific interpretability requirements.

Summary by alphaXiv.