DepthPilot: From Controllability to Interpretability in Colonoscopy Video Generation

2026-04-30 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation · Depth: Expert, long

Summary

DepthPilot is a novel, interpretable diffusion-based framework designed for generating colonoscopy videos that are both visually realistic and clinically consistent. It addresses the interpretability gap in existing controllable medical video generation by integrating two key paradigms: a Prior Distribution Alignment (PDA) strategy and an Adaptive Spline Denoising (ASD) module. The PDA strategy injects explicit depth constraints into the diffusion backbone via parameter-efficient fine-tuning, ensuring anatomical fidelity. The ASD module enhances nonlinear modeling by replacing fixed linear weights with learnable spline functions, capturing complex spatio-temporal dynamics. Evaluated across three public datasets and in-house clinical data, DepthPilot achieves FID scores below 15 and ranks first in clinician assessments, demonstrating its robust ability to produce physically consistent videos and facilitate reliable 3D reconstruction for surgical navigation.

Key takeaway

For AI Scientists developing medical video generation models, DepthPilot demonstrates that integrating explicit physical priors and advanced nonlinear modeling is crucial for achieving clinical interpretability beyond mere controllability. You should consider adopting similar geometric grounding and adaptive denoising techniques to ensure generated medical data is trustworthy and aligns with real-world anatomical and physiological constraints, thereby enabling more reliable downstream clinical applications like surgical planning.

Key insights

DepthPilot generates clinically interpretable colonoscopy videos by integrating geometric depth priors and adaptive nonlinear denoising.

Principles

• Geometric grounding enhances clinical interpretability.
• Adaptive nonlinear functions improve spatio-temporal modeling.

Method

DepthPilot uses a two-stage training paradigm: unconditional warm-up followed by an injection stage. It employs a lightweight convolutional encoder for depth sequence embedding and a distribution alignment mechanism to smoothly integrate depth features into the denoising backbone.

In practice

• Generate anatomically faithful colonoscopy videos.
• Facilitate reliable 3D reconstruction for surgical navigation.
• Aid in blind region identification during colonoscopies.

Topics

• Colonoscopy Video Generation
• AI Interpretability
• Diffusion Models
• Prior Distribution Alignment
• Adaptive Spline Denoising

Best for: AI Scientist, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.