PathoSyn: Imaging-Pathology MRI Synthesis via Disentangled Deviation Diffusion

• URL: http://arxiv.org/abs/2512.23130v1
• Date: Mon, 29 Dec 2025 01:13:50 GMT
• Title: PathoSyn: Imaging-Pathology MRI Synthesis via Disentangled Deviation Diffusion
• Authors: Jian Wang, Sixing Rong, Jiarui Xing, Yuling Xu, Weide Liu,
• Abstract summary: We present PathoSyn, a unified generative framework for Magnetic Resonance Imaging (MRI) image synthesis.<n>PathoSyn reformulates imaging-pathology as a disentangled additive deviation on a stable anatomical manifold.<n>We show that PathoSyn significantly outperforms holistic diffusion and mask-conditioned baselines in both perceptual realism and anatomical fidelity.
• Score: 11.223559964746705
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present PathoSyn, a unified generative framework for Magnetic Resonance Imaging (MRI) image synthesis that reformulates imaging-pathology as a disentangled additive deviation on a stable anatomical manifold. Current generative models typically operate in the global pixel domain or rely on binary masks, these paradigms often suffer from feature entanglement, leading to corrupted anatomical substrates or structural discontinuities. PathoSyn addresses these limitations by decomposing the synthesis task into deterministic anatomical reconstruction and stochastic deviation modeling. Central to our framework is a Deviation-Space Diffusion Model designed to learn the conditional distribution of pathological residuals, thereby capturing localized intensity variations while preserving global structural integrity by construction. To ensure spatial coherence, the diffusion process is coupled with a seam-aware fusion strategy and an inference-time stabilization module, which collectively suppress boundary artifacts and produce high-fidelity internal lesion heterogeneity. PathoSyn provides a mathematically principled pipeline for generating high-fidelity patient-specific synthetic datasets, facilitating the development of robust diagnostic algorithms in low-data regimes. By allowing interpretable counterfactual disease progression modeling, the framework supports precision intervention planning and provides a controlled environment for benchmarking clinical decision-support systems. Quantitative and qualitative evaluations on tumor imaging benchmarks demonstrate that PathoSyn significantly outperforms holistic diffusion and mask-conditioned baselines in both perceptual realism and anatomical fidelity. The source code of this work will be made publicly available.

Related papers

• Anatomically Guided Latent Diffusion for Brain MRI Progression Modeling [10.62087466710015]
  Anatomically Guided Latent Diffusion Model (AG-LDM) is a segmentation-guided framework that enforces anatomically consistent progression.<n>A lightweight 3D tissue segmentation model (WarpSeg) provides explicit anatomical supervision during both autoencoder fine-tuning and diffusion model training.
  arXiv  Detail & Related papers  (2026-01-21T01:45:36Z)
• PathoGen: Diffusion-Based Synthesis of Realistic Lesions in Histopathology Images [1.2298464939022784]
  We present PathoGen, a diffusion-based generative model that enables controllable, high-fidelity inpainting of lesions into benign histopathology images.<n>We validate PathoGen across four diverse datasets representing distinct diagnostic challenges: kidney, skin, breast, and prostate pathology.
  arXiv  Detail & Related papers  (2026-01-13T01:45:32Z)
• A Semantically Enhanced Generative Foundation Model Improves Pathological Image Synthesis [82.01597026329158]
  We introduce a Correlation-Regulated Alignment Framework for Tissue Synthesis (CRAFTS) for pathology-specific text-to-image synthesis.<n>CRAFTS incorporates a novel alignment mechanism that suppresses semantic drift to ensure biological accuracy.<n>This model generates diverse pathological images spanning 30 cancer types, with quality rigorously validated by objective metrics and pathologist evaluations.
  arXiv  Detail & Related papers  (2025-12-15T10:22:43Z)
• Unrolled Networks are Conditional Probability Flows in MRI Reconstruction [13.185194525641478]
  We introduce flow ODEs to MRI reconstruction by theoretically proving that unrolled networks are discrete implementations of conditional probability flow ODEs.<n>This connection provides explicit formulations for parameters and clarifies how intermediate states should evolve.<n>We propose Flow-Aligned Training (FLAT), which derives unrolled parameters from the ODE discretization and aligns intermediate reconstructions with the ideal ODE trajectory to improve stability and convergence.
  arXiv  Detail & Related papers  (2025-12-02T18:48:10Z)
• Moving Beyond Diffusion: Hierarchy-to-Hierarchy Autoregression for fMRI-to-Image Reconstruction [65.67001243986981]
  We propose MindHier, a coarse-to-fine fMRI-to-image reconstruction framework built on scale-wise autoregressive modeling.<n>MindHier achieves superior semantic fidelity, 4.67x faster inference, and more deterministic results than the diffusion-based baselines.
  arXiv  Detail & Related papers  (2025-10-25T15:40:07Z)
• Diffusing the Blind Spot: Uterine MRI Synthesis with Diffusion Models [7.262119921589195]
  We introduce a novel diffusion-based framework for uterine MRI synthesis.<n>Our approach generates anatomically coherent, high fidelity synthetic images that closely mimic real scans.<n>A blinded expert evaluation validates the clinical realism of our synthetic images.
  arXiv  Detail & Related papers  (2025-08-11T12:18:23Z)
• Latent Drifting in Diffusion Models for Counterfactual Medical Image Synthesis [55.959002385347645]
  Latent Drifting enables diffusion models to be conditioned for medical images fitted for the complex task of counterfactual image generation.<n>We evaluate our method on three public longitudinal benchmark datasets of brain MRI and chest X-rays for counterfactual image generation.
  arXiv  Detail & Related papers  (2024-12-30T01:59:34Z)
• LeFusion: Controllable Pathology Synthesis via Lesion-Focused Diffusion Models [42.922303491557244]
  Patient data from real-world clinical practice often suffers from data scarcity and long-tail imbalances. This study addresses these challenges by generating lesion-containing image-segmentation pairs from lesion-free images. LeFusion-generated data significantly improves the performance of state-of-the-art segmentation models.
  arXiv  Detail & Related papers  (2024-03-21T01:25:39Z)
• Steerable Conditional Diffusion for Out-of-Distribution Adaptation in Medical Image Reconstruction [75.91471250967703]
  We introduce a novel sampling framework called Steerable Conditional Diffusion.<n>This framework adapts the diffusion model, concurrently with image reconstruction, based solely on the information provided by the available measurement.<n>We achieve substantial enhancements in out-of-distribution performance across diverse imaging modalities.
  arXiv  Detail & Related papers  (2023-08-28T08:47:06Z)
• Brain Imaging-to-Graph Generation using Adversarial Hierarchical Diffusion Models for MCI Causality Analysis [44.45598796591008]
  Brain imaging-to-graph generation (BIGG) framework is proposed to map functional magnetic resonance imaging (fMRI) into effective connectivity for mild cognitive impairment analysis. The hierarchical transformers in the generator are designed to estimate the noise at multiple scales. Evaluations of the ADNI dataset demonstrate the feasibility and efficacy of the proposed model.
  arXiv  Detail & Related papers  (2023-05-18T06:54:56Z)
• Stable Deep MRI Reconstruction using Generative Priors [13.400444194036101]
  We propose a novel deep neural network based regularizer which is trained in a generative setting on reference magnitude images only. The results demonstrate competitive performance, on par with state-of-the-art end-to-end deep learning methods.
  arXiv  Detail & Related papers  (2022-10-25T08:34:29Z)
• Data-driven generation of plausible tissue geometries for realistic photoacoustic image synthesis [53.65837038435433]
  Photoacoustic tomography (PAT) has the potential to recover morphological and functional tissue properties. We propose a novel approach to PAT data simulation, which we refer to as "learning to simulate" We leverage the concept of Generative Adversarial Networks (GANs) trained on semantically annotated medical imaging data to generate plausible tissue geometries.
  arXiv  Detail & Related papers  (2021-03-29T11:30:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.