A federated learning approach to bone metastasis prediction using convolutional neural network

Abstract

Bone metastasis is a frequently occurring disease and can be a consequence of a number of different cancers such as - prostrate, lung and breast cancers, and predicting them can be really useful for the diagnosis of patients with such diseases. Classifying images of bone scan for bone metastasis prediction requires a huge amount of data to produce a prediction output which is reliable and accurate, but a single medical organization usually do not have access to such amounts of data from other organizations and those organizations are not also ready to share their patients' private data as well due to data security issues. For such scenarios, it is not often possible to train a model with enough data, thus leading to an inaccurate prediction model for bone metastasis. This can be devastating at times due to the occurrence of many false positives or false negatives, if bone metastasis is wrongly classified. In order to find a better solution, so that there is less data protection and privacy issues and therefore more availability of data, we are proposing to use a Federated Learning (FL) based approach for bone metastasis prediction using convolutional neural network. As per our knowledge and background study, we are the first to use federated learning for bone metastasis prediction on the BS-80K dataset. Federated Averaging (FedAvg) strategy was used for implementing the federated learning methodology where different client models were built along with a Global Model.