Diffusion-Based Data Augmentation for Medical Image Segmentation

• URL: http://arxiv.org/abs/2508.17844v1
• Date: Mon, 25 Aug 2025 09:49:27 GMT
• Title: Diffusion-Based Data Augmentation for Medical Image Segmentation
• Authors: Maham Nazir, Muhammad Aqeel, Francesco Setti,
• Abstract summary: DiffAug is a novel framework that combines textguided diffusion-based generation and automatic segmentation validation.<n>Our framework achieves state-of-the-art performance with 8-10% Dice improvements over baselines.
• Score: 2.841725244360927
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Medical image segmentation models struggle with rare abnormalities due to scarce annotated pathological data. We propose DiffAug a novel framework that combines textguided diffusion-based generation with automatic segmentation validation to address this challenge. Our proposed approach uses latent diffusion models conditioned on medical text descriptions and spatial masks to synthesize abnormalities via inpainting on normal images. Generated samples undergo dynamic quality validation through a latentspace segmentation network that ensures accurate localization while enabling single-step inference. The text prompts, derived from medical literature, guide the generation of diverse abnormality types without requiring manual annotation. Our validation mechanism filters synthetic samples based on spatial accuracy, maintaining quality while operating efficiently through direct latent estimation. Evaluated on three medical imaging benchmarks (CVC-ClinicDB, Kvasir-SEG, REFUGE2), our framework achieves state-of-the-art performance with 8-10% Dice improvements over baselines and reduces false negative rates by up to 28% for challenging cases like small polyps and flat lesions critical for early detection in screening applications.

Related papers

• 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)
• Zero-shot Segmentation of Skin Conditions: Erythema with Edit-Friendly Inversion [0.27624021966289597]
  This study proposes a zero-shot image segmentation framework for detecting erythema (redness of the skin) using edit-friendly inversion in diffusion models.<n>The method synthesizes reference images of the same patient that are free from erythema via generative editing and then accurately aligns these references with the original images.
  arXiv  Detail & Related papers  (2025-08-02T12:00:35Z)
• SkinDualGen: Prompt-Driven Diffusion for Simultaneous Image-Mask Generation in Skin Lesions [0.0]
  We propose a novel method that leverages the pretrained Stable Diffusion-2.0 model to generate high-quality synthetic skin lesion images.<n>A hybrid dataset combining real and synthetic data markedly enhances the performance of classification and segmentation models.
  arXiv  Detail & Related papers  (2025-07-26T15:00:37Z)
• MedDiff-FT: Data-Efficient Diffusion Model Fine-tuning with Structural Guidance for Controllable Medical Image Synthesis [19.36433173105439]
  We present MedDiff-FT, a controllable medical image generation method that fine-tunes a diffusion foundation model to produce medical images with structural dependency and domain specificity.<n>The framework effectively balances generation quality, diversity, and computational efficiency, offering a practical solution for medical data augmentation.
  arXiv  Detail & Related papers  (2025-07-01T02:22:32Z)
• Trustworthy image-to-image translation: evaluating uncertainty calibration in unpaired training scenarios [0.0]
  Mammographic screening is an effective method for detecting breast cancer, facilitating early diagnosis.<n>Deep neural networks have been shown effective in some studies, but their tendency to overfit leaves considerable risk for poor generalisation and misdiagnosis.<n>Data augmentation schemes based on unpaired neural style transfer models have been proposed that improve generalisability.<n>We evaluate their performance when trained on image patches parsed from three open access mammography datasets and one non-medical image dataset.
  arXiv  Detail & Related papers  (2025-01-29T11:09:50Z)
• 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)
• Spatial-aware Attention Generative Adversarial Network for Semi-supervised Anomaly Detection in Medical Image [63.59114880750643]
  We introduce a novel Spatial-aware Attention Generative Adrialversa Network (SAGAN) for one-class semi-supervised generation of health images. SAGAN generates high-quality health images corresponding to unlabeled data, guided by the reconstruction of normal images and restoration of pseudo-anomaly images. Extensive experiments on three medical datasets demonstrate that the proposed SAGAN outperforms the state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-05-21T15:41:34Z)
• ArSDM: Colonoscopy Images Synthesis with Adaptive Refinement Semantic Diffusion Models [69.9178140563928]
  Colonoscopy analysis is essential for assisting clinical diagnosis and treatment. The scarcity of annotated data limits the effectiveness and generalization of existing methods. We propose an Adaptive Refinement Semantic Diffusion Model (ArSDM) to generate colonoscopy images that benefit the downstream tasks.
  arXiv  Detail & Related papers  (2023-09-03T07:55:46Z)
• Diffusion Models for Counterfactual Generation and Anomaly Detection in Brain Images [39.94162291765236]
  We present a weakly supervised method to generate a healthy version of a diseased image and then use it to obtain a pixel-wise anomaly map. We employ a diffusion model trained on healthy samples and combine Denoising Diffusion Probabilistic Model (DDPM) and Denoising Implicit Model (DDIM) at each step of the sampling process.
  arXiv  Detail & Related papers  (2023-08-03T21:56:50Z)
• Towards Unbiased COVID-19 Lesion Localisation and Segmentation via Weakly Supervised Learning [66.36706284671291]
  We propose a data-driven framework supervised by only image-level labels to support unbiased lesion localisation. The framework can explicitly separate potential lesions from original images, with the help of a generative adversarial network and a lesion-specific decoder.
  arXiv  Detail & Related papers  (2021-03-01T06:05:49Z)
• Semi-supervised Medical Image Classification with Relation-driven Self-ensembling Model [71.80319052891817]
  We present a relation-driven semi-supervised framework for medical image classification. It exploits the unlabeled data by encouraging the prediction consistency of given input under perturbations. Our method outperforms many state-of-the-art semi-supervised learning methods on both single-label and multi-label image classification scenarios.
  arXiv  Detail & Related papers  (2020-05-15T06:57:54Z)

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.