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Privacy Preserving Federated Learning in Medical Imaging with Uncertainty Estimation

2024-06-18 17:35:52
Nikolas Koutsoubis, Yasin Yilmaz, Ravi P. Ramachandran, Matthew Schabath, Ghulam Rasool

Abstract

Machine learning (ML) and Artificial Intelligence (AI) have fueled remarkable advancements, particularly in healthcare. Within medical imaging, ML models hold the promise of improving disease diagnoses, treatment planning, and post-treatment monitoring. Various computer vision tasks like image classification, object detection, and image segmentation are poised to become routine in clinical analysis. However, privacy concerns surrounding patient data hinder the assembly of large training datasets needed for developing and training accurate, robust, and generalizable models. Federated Learning (FL) emerges as a compelling solution, enabling organizations to collaborate on ML model training by sharing model training information (gradients) rather than data (e.g., medical images). FL's distributed learning framework facilitates inter-institutional collaboration while preserving patient privacy. However, FL, while robust in privacy preservation, faces several challenges. Sensitive information can still be gleaned from shared gradients that are passed on between organizations during model training. Additionally, in medical imaging, quantifying model confidence\uncertainty accurately is crucial due to the noise and artifacts present in the data. Uncertainty estimation in FL encounters unique hurdles due to data heterogeneity across organizations. This paper offers a comprehensive review of FL, privacy preservation, and uncertainty estimation, with a focus on medical imaging. Alongside a survey of current research, we identify gaps in the field and suggest future directions for FL research to enhance privacy and address noisy medical imaging data challenges.

Abstract (translated)

机器学习(ML)和人工智能(AI)在医疗领域取得了显著的进步,特别是在医疗影像领域。在医疗影像领域,ML模型具有改善疾病诊断、治疗计划和治疗后监测的潜力。各种计算机视觉任务,如图像分类、目标检测和图像分割,有望成为临床分析的常规任务。然而,围绕患者数据的隐私问题阻碍了大型训练数据集的组装,这对开发和训练准确、稳健和可扩展的模型构成了阻碍。随着Federated Learning(FL)的出现,这是一种有说服力的解决方案,组织可以通过共享模型训练信息(梯度)而不是数据(例如医疗影像)来合作进行ML模型训练。FL的分布式学习框架促进了机构之间的合作,同时保留患者的隐私。然而,FL在隐私保护方面仍然面临一些挑战。可以从在模型训练过程中传递的组织之间的共享梯度中仍可能提炼出敏感信息。此外,在医疗影像中,精确量化模型的置信度(不确定性)至关重要,因为数据中存在噪声和伪影。FL中的不确定性估计遇到了独特的障碍,由于组织之间的数据异质性。本文对FL、隐私保护和不确定性估计进行了全面的回顾,重点关注医疗影像。除了对当前研究的调查外,我们指出了该领域的空白,并提出了FL研究的未来方向,以提高隐私并解决噪声医学影像数据带来的挑战。

URL

https://arxiv.org/abs/2406.12815

PDF

https://arxiv.org/pdf/2406.12815.pdf


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