FairDiffusion: Enhancing Equity in Latent Diffusion Models via Fair Bayesian Perturbation

• URL: http://arxiv.org/abs/2412.20374v1
• Date: Sun, 29 Dec 2024 06:33:37 GMT
• Title: FairDiffusion: Enhancing Equity in Latent Diffusion Models via Fair Bayesian Perturbation
• Authors: Yan Luo, Muhammad Osama Khan, Congcong Wen, Muhammad Muneeb Afzal, Titus Fidelis Wuermeling, Min Shi, Yu Tian, Yi Fang, Mengyu Wang,
• Abstract summary: We present the first comprehensive study on the fairness of medical text-to-image diffusion models. We introduce FairDiffusion, an equity-aware latent diffusion model that enhances fairness in both image generation quality and semantic correlation of clinical features. We also design and curate FairGenMed, the first dataset for studying the fairness of medical generative models.
• Score: 21.010861381369104
• License:
• Abstract: Recent progress in generative AI, especially diffusion models, has demonstrated significant utility in text-to-image synthesis. Particularly in healthcare, these models offer immense potential in generating synthetic datasets and training medical students. However, despite these strong performances, it remains uncertain if the image generation quality is consistent across different demographic subgroups. To address this critical concern, we present the first comprehensive study on the fairness of medical text-to-image diffusion models. Our extensive evaluations of the popular Stable Diffusion model reveal significant disparities across gender, race, and ethnicity. To mitigate these biases, we introduce FairDiffusion, an equity-aware latent diffusion model that enhances fairness in both image generation quality as well as the semantic correlation of clinical features. In addition, we also design and curate FairGenMed, the first dataset for studying the fairness of medical generative models. Complementing this effort, we further evaluate FairDiffusion on two widely-used external medical datasets: HAM10000 (dermatoscopic images) and CheXpert (chest X-rays) to demonstrate FairDiffusion's effectiveness in addressing fairness concerns across diverse medical imaging modalities. Together, FairDiffusion and FairGenMed significantly advance research in fair generative learning, promoting equitable benefits of generative AI in healthcare.

Related papers

• Conditional Diffusion Models are Medical Image Classifiers that Provide Explainability and Uncertainty for Free [0.7624308578421438]
  This work presents the first exploration of the potential of class conditional diffusion models for 2D medical image classification. We develop a novel majority voting scheme shown to improve the performance of medical diffusion classifiers. Experiments on the CheXpert and ISIC Melanoma skin cancer datasets demonstrate that foundation and trained-from-scratch diffusion models achieve competitive performance.
  arXiv  Detail & Related papers  (2025-02-06T00:37:21Z)
• Latent Drifting in Diffusion Models for Counterfactual Medical Image Synthesis [55.959002385347645]
  Scaling by training on large datasets has been shown to enhance the quality and fidelity of image generation and manipulation with diffusion models. Latent Drifting enables diffusion models to be conditioned for medical images fitted for the complex task of counterfactual image generation. Our results demonstrate significant performance gains in various scenarios when combined with different fine-tuning schemes.
  arXiv  Detail & Related papers  (2024-12-30T01:59:34Z)
• FairSkin: Fair Diffusion for Skin Disease Image Generation [54.29840149709033]
  Diffusion Model (DM) has become a leading method in generating synthetic medical images, but it suffers from a critical twofold bias. We propose FairSkin, a novel DM framework that mitigates these biases through a three-level resampling mechanism. Our approach significantly improves the diversity and quality of generated images, contributing to more equitable skin disease detection in clinical settings.
  arXiv  Detail & Related papers  (2024-10-29T21:37:03Z)
• Training Class-Imbalanced Diffusion Model Via Overlap Optimization [55.96820607533968]
  Diffusion models trained on real-world datasets often yield inferior fidelity for tail classes. Deep generative models, including diffusion models, are biased towards classes with abundant training images. We propose a method based on contrastive learning to minimize the overlap between distributions of synthetic images for different classes.
  arXiv  Detail & Related papers  (2024-02-16T16:47:21Z)
• DiffBoost: Enhancing Medical Image Segmentation via Text-Guided Diffusion Model [3.890243179348094]
  Large-scale, big-variant, high-quality data are crucial for developing robust and successful deep-learning models for medical applications. This paper proposes a novel approach by developing controllable diffusion models for medical image synthesis, called DiffBoost. We leverage recent diffusion probabilistic models to generate realistic and diverse synthetic medical image data.
  arXiv  Detail & Related papers  (2023-10-19T16:18:02Z)
• Analyzing Bias in Diffusion-based Face Generation Models [75.80072686374564]
  Diffusion models are increasingly popular in synthetic data generation and image editing applications. We investigate the presence of bias in diffusion-based face generation models with respect to attributes such as gender, race, and age. We examine how dataset size affects the attribute composition and perceptual quality of both diffusion and Generative Adversarial Network (GAN) based face generation models.
  arXiv  Detail & Related papers  (2023-05-10T18:22:31Z)
• Ambiguous Medical Image Segmentation using Diffusion Models [60.378180265885945]
  We introduce a single diffusion model-based approach that produces multiple plausible outputs by learning a distribution over group insights. Our proposed model generates a distribution of segmentation masks by leveraging the inherent sampling process of diffusion. Comprehensive results show that our proposed approach outperforms existing state-of-the-art ambiguous segmentation networks.
  arXiv  Detail & Related papers  (2023-04-10T17:58:22Z)
• DiffMIC: Dual-Guidance Diffusion Network for Medical Image Classification [32.67098520984195]
  We propose the first diffusion-based model (named DiffMIC) to address general medical image classification. Our experimental results demonstrate that DiffMIC outperforms state-of-the-art methods by a significant margin.
  arXiv  Detail & Related papers  (2023-03-19T09:15:45Z)
• MedSegDiff-V2: Diffusion based Medical Image Segmentation with Transformer [53.575573940055335]
  We propose a novel Transformer-based Diffusion framework, called MedSegDiff-V2. We verify its effectiveness on 20 medical image segmentation tasks with different image modalities.
  arXiv  Detail & Related papers  (2023-01-19T03:42:36Z)
• Diffusion Models for Medical Image Analysis: A Comprehensive Survey [7.272308924113656]
  Denoising diffusion models, a class of generative models, have garnered immense interest lately in various deep-learning problems. Diffusion models are widely appreciated for their strong mode coverage and quality of the generated samples. This survey intends to provide a comprehensive overview of diffusion models in the discipline of medical image analysis.
  arXiv  Detail & Related papers  (2022-11-14T23:50:52Z)

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.