SLIM-Diff: Shared Latent Image-Mask Diffusion with Lp loss for Data-Scarce Epilepsy FLAIR MRI
On the map: Diffusion models → Medical imaging and segmentation
Abstract (arXiv)
Focal cortical dysplasia (FCD) lesions in epilepsy FLAIR MRI are subtle and scarce, making joint image--mask generative modeling prone to instability and memorization. We propose SLIM-Diff, a compact joint diffusion model whose main contributions are (i) a single shared-bottleneck U-Net that enforces tight coupling between anatomy and lesion geometry from a 2-channel image+mask representation, and (ii) loss-geometry tuning via a tunable Lp objective. As an internal baseline, we include the canonical DDPM-style objective (ε-prediction with L2 loss) and isolate the effect of prediction parameterization and Lp geometry under a matched setup. Experiments show that x0-prediction is consistently the strongest choice for joint synthesis, and that fractional sub-quadratic penalties (L1.5) improve image fidelity while L2 better preserves lesion mask morphology. Our code and model weights are available in https://github.com/MarioPasc/slim-diff
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 8 of its 18 references that are in the map. First posted on arXiv 3 Feb 2026.
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- 2017A General and Adaptive Robust Loss Functioncited by 236
- 2025MedSegFactory: Text-Guided Generation of Medical Image-Mask Pairscited by 13
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