Uro-PPLD: Physiology and Pathology-Aware Latent Diffusion for Cystoscopic Image Generation and Augmentation.
Journal
IEEE journal of biomedical and health informatics
Journal Volume
PP
ISSN
2168-2208
Date Issued
2026-07-21
Author(s)
Abstract
Public cystoscopic benchmarks are limited by scarce annotations, class imbalance, and weak structural supervision. Generic augmentation often fails to preserve anatomy, color fidelity, and pathology-related variation simultaneously. To address these challenges, Uro-PPLD is proposed-a latent diffusion framework that integrates chroma-luminance decoupled attention, anatomy-guided ControlNet, continuous filling-state conditioning, and a residual pathology adapter into one denoising backbone. Topology-aware and color-consistency constraints further enforce structural plausibility and appearance fidelity. Two task-specific variants are introduced: a weakly conditioned variant for classification augmentation and a vessel-aware variant for segmentation augmentation. Experiments on EBTC, CystoDS, and BlaVeS under a unified public-benchmark protocol show that Uro-PPLD improves generation quality and yields consistent downstream gains in both classification and segmentation.
Type
journal article