64. Applications of Federated Learning in medical image segmentation and classification

64. Applications of Federated Learning in medical image segmentation and classification

Przemysław Jabłecki & Filip Ślazyk – Sano Centre for Computational Medicine, Krakow, Poland


    The advancement of machine learning techniques in critical domains such as medicine requires new approach to data distribution and privacy. Medical datasets are not easily available for research; therefore, we need new algorithmic and infrastructural solutions to overcome this limitation. One of them is Federated Learning (FL), a technique allowing for distributed training of machine learning models without actual data exchange between centres. This new method is gaining momentum in the fields of AI and medicine and more research should performed on its applications in tasks related to medical data processing.

    In our talk, we discuss the recent developments in FL for medical applications and compare existing FL frameworks in the context of their usability and reliability. We also present our recent results of large-scale, distributed FL experiments related to chest X-ray (CXR) image segmentation and classification with multi-centre data. Various aspects regarding the impact of differential privacy, data and features distributions on the training process is discussed. Infrastructural details, namely usage of HPC and cloud, are presented. Finally, we present an approach combining segmentation and classification tasks in the FL manner.

    About the authors

    Przemysław Jabłecki holds an engineer degree in Computer Science. He was graduated with honours from AGH Krakow in 2021. His engineer thesis was focused on the integration and comparison of feature selection algorithms in the context of data of patients suffering from hairy cell leukaemia. Currently, he is pursuing a MSc degree in Data Science and doing research on the application of federated learning to medical image segmentation. He is interested in deep learning, algorithms, and large-scale cloud computing.

    Filip Ślazyk holds a engineer degree in Computer Science obtained at AGH Krakow in 2021. Currently, he is working on a MSc degree in Data Science at the same university. His engineer thesis was related to processing the data of hairy cell leukaemia patients using machine learning techniques. His current research are focused on the applications of federated learning in healthcare under the supervision of Maciej Malawski. He is interested in machine learning, deep learning and cloud computing. His professional experience has been gained at the key players in the IT industry. He is the Junior Scientific Programmer at the Sano Centre.