MedCondDiff: Lightweight, Robust, Semantically Guided Diffusion for Medical Image Segmentation

• URL: http://arxiv.org/abs/2512.00350v1
• Date: Sat, 29 Nov 2025 06:43:15 GMT
• Title: MedCondDiff: Lightweight, Robust, Semantically Guided Diffusion for Medical Image Segmentation
• Authors: Ruirui Huang, Jiacheng Li,
• Abstract summary: We introduce MedCondDiff, a diffusion-based framework for medical image segmentation.<n>The model conditions the denoising process on semantic priors extracted by a Pyramid Vision Transformer (PVT) backbone.<n>This design improves robustness while reducing both inference time and VRAM usage.
• Score: 5.838464931565891
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce MedCondDiff, a diffusion-based framework for multi-organ medical image segmentation that is efficient and anatomically grounded. The model conditions the denoising process on semantic priors extracted by a Pyramid Vision Transformer (PVT) backbone, yielding a semantically guided and lightweight diffusion architecture. This design improves robustness while reducing both inference time and VRAM usage compared to conventional diffusion models. Experiments on multi-organ, multi-modality datasets demonstrate that MedCondDiff delivers competitive performance across anatomical regions and imaging modalities, underscoring the potential of semantically guided diffusion models as an effective class of architectures for medical imaging tasks.

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