FairSkin: Fair Diffusion for Skin Disease Image Generation

• URL: http://arxiv.org/abs/2410.22551v2
• Date: Thu, 31 Oct 2024 16:04:48 GMT
• Title: FairSkin: Fair Diffusion for Skin Disease Image Generation
• Authors: Ruichen Zhang, Yuguang Yao, Zhen Tan, Zhiming Li, Pan Wang, Huan Liu, Jingtong Hu, Sijia Liu, Tianlong Chen,
• Abstract summary: 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.
• Score: 54.29840149709033
• License:
• Abstract: Image generation is a prevailing technique for clinical data augmentation for advancing diagnostic accuracy and reducing healthcare disparities. Diffusion Model (DM) has become a leading method in generating synthetic medical images, but it suffers from a critical twofold bias: (1) The quality of images generated for Caucasian individuals is significantly higher, as measured by the Frechet Inception Distance (FID). (2) The ability of the downstream-task learner to learn critical features from disease images varies across different skin tones. These biases pose significant risks, particularly in skin disease detection, where underrepresentation of certain skin tones can lead to misdiagnosis or neglect of specific conditions. To address these challenges, we propose FairSkin, a novel DM framework that mitigates these biases through a three-level resampling mechanism, ensuring fairer representation across racial and disease categories. Our approach significantly improves the diversity and quality of generated images, contributing to more equitable skin disease detection in clinical settings.

Related papers

• Evaluating Machine Learning-based Skin Cancer Diagnosis [0.0]
  The research assesses two convolutional neural network architectures: a MobileNet-based model and a custom CNN model. Both models are evaluated for their ability to classify skin lesions into seven categories and to distinguish between dangerous and benign lesions. The study concludes that while the models show promise in explainability, further development is needed to ensure fairness across different skin tones.
  arXiv  Detail & Related papers  (2024-09-04T02:44:48Z)
• A Clinical-oriented Multi-level Contrastive Learning Method for Disease Diagnosis in Low-quality Medical Images [4.576524795036682]
  Disease diagnosis methods guided by contrastive learning (CL) have shown significant advantages in lesion feature representation. We propose a clinical-oriented multi-level CL framework that aims to enhance the model's capacity to extract lesion features. The proposed CL framework is validated on two public medical image datasets, EyeQ and Chest X-ray.
  arXiv  Detail & Related papers  (2024-04-07T09:08:14Z)
• Optimizing Skin Lesion Classification via Multimodal Data and Auxiliary Task Integration [54.76511683427566]
  This research introduces a novel multimodal method for classifying skin lesions, integrating smartphone-captured images with essential clinical and demographic information. A distinctive aspect of this method is the integration of an auxiliary task focused on super-resolution image prediction. The experimental evaluations have been conducted using the PAD-UFES20 dataset, applying various deep-learning architectures.
  arXiv  Detail & Related papers  (2024-02-16T05:16:20Z)
• On the notion of Hallucinations from the lens of Bias and Validity in Synthetic CXR Images [0.35998666903987897]
  Generative models, such as diffusion models, aim to mitigate data quality and clinical information disparities. At Stanford, researchers explored the utility of a fine-tuned Stable Diffusion model (RoentGen) for medical imaging data augmentation. We leveraged RoentGen to produce synthetic Chest-XRay (CXR) images and conducted assessments on bias, validity, and hallucinations.
  arXiv  Detail & Related papers  (2023-12-12T04:41:20Z)
• EdgeMixup: Improving Fairness for Skin Disease Classification and Segmentation [9.750368551427494]
  Skin lesions can be an early indicator of a wide range of infectious and other diseases. The use of deep learning (DL) models to diagnose skin lesions has great potential in assisting clinicians with prescreening patients. These models often learn biases inherent in training data, which can lead to a performance gap in the diagnosis of people with light and/or dark skin tones.
  arXiv  Detail & Related papers  (2022-02-28T15:33:31Z)
• Texture Characterization of Histopathologic Images Using Ecological Diversity Measures and Discrete Wavelet Transform [82.53597363161228]
  This paper proposes a method for characterizing texture across histopathologic images with a considerable success rate. It is possible to quantify the intrinsic properties of such images with promising accuracy on two HI datasets.
  arXiv  Detail & Related papers  (2022-02-27T02:19:09Z)
• Automatic Classification of Neuromuscular Diseases in Children Using Photoacoustic Imaging [77.32032399775152]
  Neuromuscular diseases (NMDs) cause a significant burden for both healthcare systems and society. They can lead to severe progressive muscle weakness, muscle degeneration, contracture, deformity and progressive disability.
  arXiv  Detail & Related papers  (2022-01-27T16:37:19Z)
• Malignancy Prediction and Lesion Identification from Clinical Dermatological Images [65.1629311281062]
  We consider machine-learning-based malignancy prediction and lesion identification from clinical dermatological images. We first identify all lesions present in the image regardless of sub-type or likelihood of malignancy, then it estimates their likelihood of malignancy, and through aggregation, it also generates an image-level likelihood of malignancy.
  arXiv  Detail & Related papers  (2021-04-02T20:52:05Z)
• Variational Knowledge Distillation for Disease Classification in Chest X-Rays [102.04931207504173]
  We propose itvariational knowledge distillation (VKD), which is a new probabilistic inference framework for disease classification based on X-rays. We demonstrate the effectiveness of our method on three public benchmark datasets with paired X-ray images and EHRs.
  arXiv  Detail & Related papers  (2021-03-19T14:13:56Z)
• Quantifying and Leveraging Predictive Uncertainty for Medical Image Assessment [13.330243305948278]
  We propose a system that learns not only the probabilistic estimate for classification, but also an explicit uncertainty measure. We argue that this approach is essential to account for the inherent ambiguity characteristic of medical images from different radiologic exams. In our experiments we demonstrate that sample rejection based on the predicted uncertainty can significantly improve the ROC-AUC for various tasks.
  arXiv  Detail & Related papers  (2020-07-08T16:47:55Z)

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.