# Exploring Federated Deep Learning for Standardising Naming Conventions in Radiotherapy Data

arXiv: https://arxiv.org/abs/2402.08999 · HTML: https://mlsanity.com/paper/2402.08999/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 49 references that are in the map.
- First posted on arXiv 14 Feb 2024.

## Builds on (most cited first, 13 of 13)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Adaptive Federated Optimization](https://mlsanity.com/paper/2003.00295/) (2020)
- [Generalizing from a Few Examples: A Survey on Few-Shot Learning](https://mlsanity.com/paper/1904.05046/) (2019)
- [Flower: A Friendly Federated Learning Research Framework](https://mlsanity.com/paper/2007.14390/) (2020)
- [FedML: A Research Library and Benchmark for Federated Machine Learning](https://mlsanity.com/paper/2007.13518/) (2020)
- [Federated Learning Enables Big Data for Rare Cancer Boundary Detection](https://mlsanity.com/paper/2204.10836/) (2022)
- [OpenFL: An open-source framework for Federated Learning](https://mlsanity.com/paper/2105.06413/) (2021)
- [Orchestra: Unsupervised Federated Learning via Globally Consistent Clustering](https://mlsanity.com/paper/2205.11506/) (2022)
- [Cross-Silo Federated Learning: Challenges and Opportunities](https://mlsanity.com/paper/2206.12949/) (2022)
- [Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer](https://mlsanity.com/paper/1703.08516/) (2017)
- [Towards automated patient data cleaning using deep learning: A feasibility study on the standardization of organ labeling](https://mlsanity.com/paper/1801.00096/) (2017)
- [Mining Domain Knowledge: Improved Framework towards Automatically Standardizing Anatomical Structure Nomenclature in Radiotherapy](https://mlsanity.com/paper/1912.02084/) (2019)

## Abstract (arXiv)

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.