Benchmarking Collaborative Learning Methods Cost-Effectiveness for Prostate Segmentation

Lucia Innocenti, Michela Antonelli, Francesco Cremonesi, Kenaan Sarhan, Alejandro Granados, Vicky Goh, Sébastien Ourselin, Marco Lorenzi

Abstract: Healthcare data is often split into medium/small-sized collections across multiple hospitals and access to it is encumbered by privacy regulations. This brings difficulties to use them for the development of machine learning and deep learning models, which are known to be data-hungry. One way to overcome this limitation is to use collaborative learning (CL) methods, which allow hospitals to work collaboratively to solve a task, without the need to explicitly share local data.