In the era of rapidly advancing medical technologies, the segmentation of
medical data has become inevitable, necessitating the development of privacy
preserving machine learning algorithms that can train on distributed data.
Consolidating sensitive medical data is not always an option particularly due
to the stringent privacy regulations imposed by the Health Insurance
Portability and Accountability Act (HIPAA). In this paper, we introduce a HIPAA
compliant framework that can train from distributed data. We then propose a
multimodal vertical federated model for Alzheimer's Disease (AD) detection, a
serious neurodegenerative condition that can cause dementia, severely impairing
brain function and hindering simple tasks, especially without preventative
care. This vertical federated model offers a distributed architecture that
enables collaborative learning across diverse sources of medical data while
respecting privacy constraints imposed by HIPAA. It is also able to leverage
multiple modalities of data, enhancing the robustness and accuracy of AD
detection. Our proposed model not only contributes to the advancement of
federated learning techniques but also holds promise for overcoming the hurdles
posed by data segmentation in medical research. By using vertical federated
learning, this research strives to provide a framework that enables healthcare
institutions to harness the collective intelligence embedded in their
distributed datasets without compromising patient privacy.

这篇论文介绍了一种能够在分布式数据上训练的符合 HIPAA 规范的框架，并提出了一个用于阿尔茨海默病（AD）检测的多模态垂直联邦模型。这种垂直联邦模型能够在不违反 HIPAA 所施加的隐私限制的情况下，实现跨不同源医学数据的协同学习，并利用多种数据模态提高 AD 检测的稳健性和准确性。该研究旨在通过使用垂直联邦学习，提供一个框架使医疗机构能够利用分布式数据集中蕴含的集体智慧，而不会损害患者隐私。

多模态数据上的纵向联邦阿尔茨海默病检测

Vertical Federated Alzheimer's Detection on Multimodal Data

With the maturity achieved by deep learning techniques, intelligent systems
that can assist physicians in the daily interpretation of clinical images can
play a very important role. In addition, quantum techniques applied to deep
learning can enhance this performance, and federated learning techniques can
realize privacy-friendly collaborative learning among different participants,
solving privacy issues due to the use of sensitive data and reducing the number
of data to be collected for each individual participant. We present in this
study a hybrid quantum neural network that can be used to quantify
non-alcoholic liver steatosis and could be useful in the diagnostic process to
determine a liver's suitability for transplantation; at the same time, we
propose a federated learning approach based on a classical deep learning
solution to solve the same problem, but using a reduced data set in each part.
The liver steatosis image classification accuracy of the hybrid quantum neural
network, the hybrid quantum ResNet model, consisted of 5 qubits and more than
100 variational gates, reaches 97%, which is 1.8% higher than its classical
counterpart, ResNet. Crucially, that even with a reduced dataset, our hybrid
approach consistently outperformed its classical counterpart, indicating
superior generalization and less potential for overfitting in medical
applications. In addition, a federated approach with multiple clients, up to
32, despite the lower accuracy, but still higher than 90%, would allow using,
for each participant, a very small dataset, i.e., up to one-thirtieth. Our
work, based over real-word clinical data can be regarded as a scalable and
collaborative starting point, could thus fulfill the need for an effective and
reliable computer-assisted system that facilitates the daily diagnostic work of
the clinical pathologist.

使用深度学习技术的成熟度和量子技术的应用，我们提出了一个混合量子神经网络和联邦学习方法来解决非酒精性脂肪肝的图像分类问题，并展示了其在医疗应用中的出色性能和潜力。