Distributed collaborative machine learning in real-world application scenario: A white blood cell subtypes classification case study

Abstract

White blood cell (WBC) subtype classification is a critical step in monitoring an individual’s health. However, it remains a challenging task due to the significant morphological variability of WBCs and the domain shift introduced by differing acquisition protocols across hospitals. Numerous approaches have been proposed to mitigate domain shift, including supervised and unsupervised domain adaptation, as well as domain generalisation. These methods, however, require a suitable amount of representative target images, even if unlabelled, or a suitable amount of images from multiple sources, which may not be feasible due to privacy regulations. In this study, we explore an alternative paradigm, known as Distributed Collaborative Machine Learning (DCML), which consists of exploiting images from different sources in a privacy-preserving setup. Although DCML methods seem well suited to this application, to the best of our knowledge, they have not been used for this task or to address the above-mentioned issues. However, we argue that DCML deserves further consideration in medical images as a potential alternative solution against domain shift in a privacy-preserving setup. To substantiate our view, we consider three DCML methods: early and late fusion and federated learning approaches, each offering distinct trade-offs in terms of training constraints, computational overhead and communications costs. We then conduct an extensive, cross-dataset experimental evaluation on four benchmark datasets and provide evidence that even simple implementations of DCML methods can effectively mitigate domain shift in WBC classification tasks.