Private, fair and accurate: Training large-scale, privacy-preserving AI models in medical imaging
University Hospital RWTH Aachen（亚琛工业大学附属医院）；Technical University of Munich（慕尼黑工业大学）
Soroosh Tayebi Arasteh, Alexander Ziller, Christiane Kuhl, Marcus Makowski, Sven Nebelung, Rickmer Braren, Daniel Rueckert, Daniel Truhn, Georgios Kaissis
备注：
Published in Communications Medicine. Nature Portfolio
Artificial intelligence (AI) models are increasingly used in the medical domain. However, as medical data is highly sensitive, special precautions to ensure its protection are required. The gold standard for privacy preservation is the introduction of differential privacy (DP) to model training. Prior work indicates that DP has negative implications on model accuracy and fairness, which are unacceptable in medicine and represent a main barrier to the widespread use of privacy-preserving techniques. In this work, we evaluated the effect of privacy-preserving training of AI models regarding accuracy and fairness compared to non-private training. For this, we used two datasets: (1) A large dataset (N=193,311) of high quality clinical chest radiographs, and (2) a dataset (N=1,625) of 3D abdominal computed tomography (CT) images, with the task of classifying the presence of pancreatic ductal adenocarcinoma (PDAC). Both were retrospectively collected and manually labeled by experienced radiologists. We then compared non-private deep convolutional neural networks (CNNs) and privacy-preserving (DP) models with respect to privacy-utility trade-offs measured as area under the receiver-operator-characteristic curve (AUROC), and privacy-fairness trade-offs, measured as Pearson's r or Statistical Parity Difference. We found that, while the privacy-preserving trainings yielded lower accuracy, they did largely not amplify discrimination against age, sex or co-morbidity. 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.
Benchmarking Differentially Private Residual Networks for Medical Imagery
OpenMined；Manifold Computing Group（曼尼福德计算集团）；Ford R&A（福特研发部门）；Harvard University（哈佛大学）；Indian Institute of Technology Jodhpur（印度理工学院焦特布尔分校）；University of Oxford（牛津大学）
A Fairness Analysis on Private Aggregation of Teacher Ensembles
Syracuse University（锡拉丘兹大学）
教师集合私有聚合（Private Aggregation of Teacher Ensembles，PATE）是一个重要的私有机器学习框架。它结合了多个学习模型作为教师的学生模型，学习预测教师之间的嘈杂投票选择的输出。由此产生的模型满足差分隐私，并已被证明有效地学习高质量的私人模型在半监督设置或当一个人希望保护数据标签。本文问这种隐私保护框架是否会引入或加剧偏见和不公平，并表明PATE可以引入个人和群体之间的准确性差距。本文分析了哪些算法和数据属性造成了不成比例的影响，为什么这些方面对不同群体的影响不成比例，并提出了减轻这些影响的指导方针。所提出的方法进行了评估，在几个数据集和设置。
How to Democratise and Protect AI: Fair and Differentially Private Decentralised Deep Learning
National University of Singapore（新加坡国立大学）；The University of Melbourne（墨尔本大学）；IBM Singapore Lab（IBM新加坡实验室）；Monash University（莫纳什大学）；Deakin University（迪肯大学）
FAIRPLAI: A Human-in-the-Loop Approach to Fair and Private Machine Learning
Dept. of Computer Science, The University of Texas at El Paso（得克萨斯大学埃尔帕索分校计算机科学系）；Dept. of Mathematics, Mountain View High School（山景高中数学系）；Dept. of Mathematics, El Paso Independent School District（埃尔帕索独立学区数学系）
FairDomain: Achieving Fairness in Cross-Domain Medical Image Segmentation and Classification
Center for Artificial Intelligence and Robotics, New York University Abu Dhabi（纽约大学阿布扎比分校人工智能与机器人中心）；New York University（纽约大学）；Harvard University（哈佛大学）
FairFedMed: Benchmarking Group Fairness in Federated Medical Imaging with FairLoRA
Harvard AI and Robotics Lab（哈佛人工智能与机器人实验室）；Harvard University（哈佛大学）；Harvard Ophthalmology AI lab（哈佛眼科人工智能实验室）；University of Central Florida（佛罗里达中央大学）；University of Louisiana at Lafayette（路易斯安那州立大学拉斐特分校）；New York University（纽约大学）；NYUAD Center for Artificial Intelligence and Robotics（纽约大学阿布扎克人工智能与机器人中心）
The Boundaries of Fair AI in Medical Image Prognosis: A Causal Perspective
Department of Computer Science and Engineering, The Ohio State University（计算机科学与工程系，俄亥俄州立大学）；Department of Biomedical Informatics, The Ohio State University（生物医学信息学系，俄亥俄州立大学）；Department of Ophthalmology and Visual Sciences, The Ohio State University（眼科学与视觉科学系，俄亥俄州立大学）
A New Perspective to Boost Performance Fairness for Medical Federated Learning
Australian National University（澳大利亚国立大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Guangxi Medical University（广西医科大学）；Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局高性能计算研究所）；Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)（穆罕默德·本·扎耶德人工智能大学）；The Hong Kong University of Science and Technology（香港科技大学）
Differentially Private ADMM for Distributed Medical Machine Learning
University of Houston（休斯顿大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校）；University of Science and Technology of China（中国科学技术大学）
由于大量数据分布在多个位置，分布式机器学习吸引了大量的研究兴趣。交替方向乘法（Alternating Direction Method of Multipliers，ADMM）是一种设计分布式机器学习算法的有效方法，每个Agent在本地数据集上进行计算，并通过迭代过程与相邻Agent交换计算结果。如果本地数据是敏感的，则在此迭代过程中存在显著的隐私泄漏。在本文中，我们提出了一个差分隐私ADMM算法（P-ADMM）提供动态零集中差分隐私（动态zCDP），通过插入高斯噪声线性衰减的方差。我们证明了P-ADMM与非私有对应方相比具有相同的收敛速度，即，$\mathcal{O}(1/K)$，其中$K$是一般凸和强凸问题的迭代次数和线性收敛，同时提供差分私有保证。此外，通过我们在真实世界数据集上进行的实验，我们的经验表明，P-ADMM具有最好的性能在现有的差分私有ADMM为基础的算法。
Fairness in Multi-modal Medical Diagnosis with Demonstration Selection
Arizona State University（亚利桑那州立大学）；University of Rochester（罗切斯特大学）；University of Virginia（弗吉尼亚大学）；UCL（伦敦大学学院）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；University of Central Florida（佛罗里达中央大学）
FairMedFM: Fairness Benchmarking for Medical Imaging Foundation Models
The University of British Columbia（不列颠哥伦比亚大学）；Vector Institute（矢量研究所）；University of Science and Technology of China（中国科学技术大学）；The Chinese University of Hong Kong（香港中文大学）
Fair Federated Medical Image Classification Against Quality Shift via Inter-Client Progressive State Matching
School of Electronic Information and Communications, Huazhong University of Science and Technology（华中科技大学电子信息与通信学院）；Lassonde School of Engineering, York University（约克大学拉萨onde工程学院）
