Exploring Federated Deep Learning for Standardising Naming Conventions in Radiotherapy Data

📄 arXiv: 2402.08999v1 📥 PDF

作者: Ali Haidar, Daniel Al Mouiee, Farhannah Aly, David Thwaites, Lois Holloway

分类: cs.LG, physics.med-ph

发布日期: 2024-02-14

💡 一句话要点

提出联邦深度学习以标准化放射治疗数据命名规范

🎯 匹配领域: 支柱九：具身大模型 (Embodied Foundation Models)

关键词: 放射治疗 联邦学习 深度学习 多模态融合 数据标准化 医疗数据 机器学习

📋 核心要点

  1. 现有方法未考虑放射治疗患者记录分布于多个数据中心的情况，导致标准化过程效率低下。
  2. 本文提出通过联邦学习和多模态深度神经网络，自动化标准化放射治疗数据命名的解决方案。
  3. 实验结果显示，表格-体积模型在分类准确性上优于单模态模型，并与集中式模型表现相当。

📝 摘要（中文）

标准化放射治疗数据中的结构体积名称对于数据挖掘和分析至关重要，尤其是在多机构中心之间。然而，现有方法在处理分布于多个数据中心的患者记录时存在不足。本文提出了一种新方法，通过集成去中心化的实时数据和联邦学习，模拟真实环境以建立标准化命名规范。研究中使用了多模态深度神经网络，并提取了表格、视觉和体积三种属性进行模型训练。实验结果表明，融合多种模态能够显著提升模型性能，尤其是表格-体积模型表现更佳，同时与集中式设置下的模型准确性相当。

🔬 方法详解

问题定义：本文旨在解决放射治疗数据中结构体积名称标准化的问题。现有方法在处理分布于多个数据中心的患者记录时，效率低且资源消耗大。

核心思路：通过引入联邦学习和多模态深度学习，模拟真实环境以实现数据命名的标准化，充分利用分散的数据资源。

技术框架：整体架构包括数据收集、特征提取（表格、视觉、体积）、模型训练（多模态深度神经网络）和性能评估等模块，采用联邦学习进行分布式训练。

关键创新：本研究的创新在于首次将联邦学习应用于放射治疗数据的标准化任务，强调了多模态数据融合的重要性。

关键设计：模型训练中采用了多种输入模态，损失函数设计考虑了分类准确性，网络结构则基于深度神经网络的多模态特性进行优化。实验中还进行了消融分析，以评估数据中心数量和样本总数对训练过程的影响。

📊 实验亮点

实验结果显示，表格-体积模型在分类准确性上优于单模态模型，且与集中式模型的准确性相当，证明了联邦学习在标准化任务中的适用性。具体而言，模型在多种场景下的表现均显示出显著的性能提升，尤其是在多数据中心的设置中。

🎯 应用场景

该研究的潜在应用领域包括医疗数据管理、放射治疗计划优化和多机构合作研究。通过标准化命名规范，可以提高数据共享和分析的效率，促进跨机构的合作与研究，最终提升患者治疗效果。

📄 摘要（原文）

Standardising structure volume names in radiotherapy (RT) data is necessary to enable data mining and analyses, especially across multi-institutional centres. This process is time and resource intensive, which highlights the need for new automated and efficient approaches to handle the task. Several machine learning-based methods have been proposed and evaluated to standardise nomenclature. However, no studies have considered that RT patient records are distributed across multiple data centres. This paper introduces a method that emulates real-world environments to establish standardised nomenclature. This is achieved by integrating decentralised real-time data and federated learning (FL). A multimodal deep artificial neural network was proposed to standardise RT data in federated settings. Three types of possible attributes were extracted from the structures to train the deep learning models: tabular, visual, and volumetric. Simulated experiments were carried out to train the models across several scenarios including multiple data centres, input modalities, and aggregation strategies. The models were compared against models developed with single modalities in federated settings, in addition to models trained in centralised settings. Categorical classification accuracy was calculated on hold-out samples to inform the models performance. Our results highlight the need for fusing multiple modalities when training such models, with better performance reported with tabular-volumetric models. In addition, we report comparable accuracy compared to models built in centralised settings. This demonstrates the suitability of FL for handling the standardization task. Additional ablation analyses showed that the total number of samples in the data centres and the number of data centres highly affects the training process and should be carefully considered when building standardisation models.