PCaPaint: Prostate Cancer Inpainting by Mitigating Shortcut Learning

PCaPaint is a prostate cancer inpainting method that uses latent diffusion models (LDMs) and introduces a conditioning strategy where the condition image is filled with Gaussian noise to mitigate shortcut learning. It also proposes a new training objective that focuses on errors within the lesion region and a multi‑sequence latent design that separately compresses T2w and DWI&ADC scans to preserve their distinct frequency characteristics. Experiments show that the synthetic data generated by PCaPaint improves downstream tasks such as prostate lesion segmentation, patient‑level classification, and lesion‑level detection, outperforming recent state‑of‑the‑art LDM‑based tumor inpainting methods in both performance and image quality.

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• diffusion
• computer-vision
• benchmarks