Few-Shot Generation of Brain Tumors for Secure and Fair Data Sharing

• URL: http://arxiv.org/abs/2504.00150v1
• Date: Mon, 31 Mar 2025 18:59:15 GMT
• Title: Few-Shot Generation of Brain Tumors for Secure and Fair Data Sharing
• Authors: Yongyi Shi, Ge Wang,
• Abstract summary: This study proposes a decentralized few-shot generative model (DFGM) to synthesize brain tumor images while preserving privacy.<n> DFGM harmonizes private tumor data with publicly shareable healthy images from multiple medical centers, constructing a new dataset by blending tumor foregrounds with healthy backgrounds.<n>We assess DFGM's effectiveness in brain tumor segmentation using a UNet, achieving Dice score improvements of 3.9% for data augmentation and 4.6% for fairness on a separate dataset.
• Score: 5.101848799297469
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Leveraging multi-center data for medical analytics presents challenges due to privacy concerns and data heterogeneity. While distributed approaches such as federated learning has gained traction, they remain vulnerable to privacy breaches, particularly in sensitive domains like medical imaging. Generative models, such as diffusion models, enhance privacy by synthesizing realistic data. However, they are prone to memorization, especially when trained on small datasets. This study proposes a decentralized few-shot generative model (DFGM) to synthesize brain tumor images while fully preserving privacy. DFGM harmonizes private tumor data with publicly shareable healthy images from multiple medical centers, constructing a new dataset by blending tumor foregrounds with healthy backgrounds. This approach ensures stringent privacy protection and enables controllable, high-quality synthesis by preserving both the healthy backgrounds and tumor foregrounds. We assess DFGM's effectiveness in brain tumor segmentation using a UNet, achieving Dice score improvements of 3.9% for data augmentation and 4.6% for fairness on a separate dataset.

Related papers

• FedDP: Privacy-preserving method based on federated learning for histopathology image segmentation [2.864354559973703]
  This paper addresses the dispersed nature and privacy sensitivity of medical image data by employing a federated learning framework. The proposed method, FedDP, minimally impacts model accuracy while effectively safeguarding the privacy of cancer pathology image data.
  arXiv  Detail & Related papers  (2024-11-07T08:02:58Z)
• 3D MRI Synthesis with Slice-Based Latent Diffusion Models: Improving Tumor Segmentation Tasks in Data-Scarce Regimes [2.8498944632323755]
  We propose a novel slice-based latent diffusion architecture to address the complexities of volumetric data generation. This approach extends the joint distribution modeling of medical images and their associated masks, allowing a simultaneous generation of both under data-scarce regimes. Our architecture can be conditioned by tumor characteristics, including size, shape, and relative position, thereby providing a diverse range of tumor variations.
  arXiv  Detail & Related papers  (2024-06-08T09:53:45Z)
• 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.<n>This paper proposes a novel approach by developing controllable diffusion models for medical image synthesis, called DiffBoost.<n>We leverage recent diffusion probabilistic models to generate realistic and diverse synthetic medical image data.
  arXiv  Detail & Related papers  (2023-10-19T16:18:02Z)
• Source-Free Collaborative Domain Adaptation via Multi-Perspective Feature Enrichment for Functional MRI Analysis [55.03872260158717]
  Resting-state MRI functional (rs-fMRI) is increasingly employed in multi-site research to aid neurological disorder analysis. Many methods have been proposed to reduce fMRI heterogeneity between source and target domains. But acquiring source data is challenging due to concerns and/or data storage burdens in multi-site studies. We design a source-free collaborative domain adaptation framework for fMRI analysis, where only a pretrained source model and unlabeled target data are accessible.
  arXiv  Detail & Related papers  (2023-08-24T01:30:18Z)
• Differential Privacy for Adaptive Weight Aggregation in Federated Tumor Segmentation [0.16746114653388383]
  Federated Learning (FL) is a distributed machine learning approach that safeguards privacy by creating an impartial global model while respecting the privacy of individual client data. We present a differential privacy (DP) federated deep learning framework in medical image segmentation. We extend our similarity weight aggregation (SimAgg) method to DP-SimAgg algorithm, a differentially private similarity-weighted aggregation algorithm for brain tumor segmentation.
  arXiv  Detail & Related papers  (2023-08-01T21:59:22Z)
• Private, fair and accurate: Training large-scale, privacy-preserving AI models in medical imaging [47.99192239793597]
  We evaluated the effect of privacy-preserving training of AI models regarding accuracy and fairness compared to non-private training. Our study shows that -- under the challenging realistic circumstances of a real-life clinical dataset -- the privacy-preserving training of diagnostic deep learning models is possible with excellent diagnostic accuracy and fairness.
  arXiv  Detail & Related papers  (2023-02-03T09:49:13Z)
• Brain Tumor Synthetic Data Generation with Adaptive StyleGANs [6.244557340851846]
  We present a method to generate brain tumor MRI images using generative adversarial networks. Results demonstrate that the proposed method can learn the distributions of brain tumors. The approach can addresses the limited data availability by generating realistic-looking brain MRI with tumors.
  arXiv  Detail & Related papers  (2022-12-04T09:01:33Z)
• Federated Learning Enables Big Data for Rare Cancer Boundary Detection [98.5549882883963]
  We present findings from the largest Federated ML study to-date, involving data from 71 healthcare institutions across 6 continents. We generate an automatic tumor boundary detector for the rare disease of glioblastoma. We demonstrate a 33% improvement over a publicly trained model to delineate the surgically targetable tumor, and 23% improvement over the tumor's entire extent.
  arXiv  Detail & Related papers  (2022-04-22T17:27:00Z)
• FedMed-GAN: Federated Domain Translation on Unsupervised Cross-Modality Brain Image Synthesis [55.939957482776194]
  We propose a new benchmark for federated domain translation on unsupervised brain image synthesis (termed as FedMed-GAN) FedMed-GAN mitigates the mode collapse without sacrificing the performance of generators. A comprehensive evaluation is provided for comparing FedMed-GAN and other centralized methods.
  arXiv  Detail & Related papers  (2022-01-22T02:50:29Z)
• Cross-Modality Deep Feature Learning for Brain Tumor Segmentation [158.8192041981564]
  This paper proposes a novel cross-modality deep feature learning framework to segment brain tumors from the multi-modality MRI data. The core idea is to mine rich patterns across the multi-modality data to make up for the insufficient data scale. Comprehensive experiments are conducted on the BraTS benchmarks, which show that the proposed cross-modality deep feature learning framework can effectively improve the brain tumor segmentation performance.
  arXiv  Detail & Related papers  (2022-01-07T07:46:01Z)
• A Deep Learning Approach to Private Data Sharing of Medical Images Using Conditional GANs [1.2099130772175573]
  We present a method for generating a synthetic dataset based on COSENTYX (secukinumab) Ankylosing Spondylitis clinical study. In this paper, we present a method for generating a synthetic dataset and conduct an in-depth analysis on its properties of along three key metrics: image fidelity, sample diversity and dataset privacy.
  arXiv  Detail & Related papers  (2021-06-24T17:24:06Z)
• SAG-GAN: Semi-Supervised Attention-Guided GANs for Data Augmentation on Medical Images [47.35184075381965]
  We present a data augmentation method for generating synthetic medical images using cycle-consistency Generative Adversarial Networks (GANs) The proposed GANs-based model can generate a tumor image from a normal image, and in turn, it can also generate a normal image from a tumor image. We train the classification model using real images with classic data augmentation methods and classification models using synthetic images.
  arXiv  Detail & Related papers  (2020-11-15T14:01:24Z)
• GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators [74.16405337436213]
  We propose Gradient-sanitized Wasserstein Generative Adrial Networks (GS-WGAN) GS-WGAN allows releasing a sanitized form of sensitive data with rigorous privacy guarantees. We find our approach consistently outperforms state-of-the-art approaches across multiple metrics.
  arXiv  Detail & Related papers  (2020-06-15T10:01:01Z)

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.