Exploring Federated Deep Learning for Standardising Naming Conventions in Radiotherapy Data

• URL: http://arxiv.org/abs/2402.08999v1
• Date: Wed, 14 Feb 2024 07:52:28 GMT
• Title: Exploring Federated Deep Learning for Standardising Naming Conventions in Radiotherapy Data
• Authors: Ali Haidar, Daniel Al Mouiee, Farhannah Aly, David Thwaites, Lois Holloway
• Abstract summary: Standardising structure volume names in radiotherapy (RT) data is necessary to enable data mining and analyses. 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. A multimodal deep artificial neural network was proposed to standardise RT data in federated settings.
• Score: 0.18749305679160366
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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