Benchmarking Collaborative Learning Methods Cost-Effectiveness for Prostate Segmentation
This work addresses data privacy and fragmentation issues in medical imaging for hospitals, though it is incremental as it applies existing methods to a new domain.
The paper tackled the problem of prostate segmentation from MRI in a collaborative healthcare setting by comparing federated learning and consensus-based methods, finding that consensus-based methods provide equal or better results while being highly cost-effective.
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. In this paper, we address a prostate segmentation problem from MRI in a collaborative scenario by comparing two different approaches: federated learning (FL) and consensus-based methods (CBM). To the best of our knowledge, this is the first work in which CBM, such as label fusion techniques, are used to solve a problem of collaborative learning. In this setting, CBM combine predictions from locally trained models to obtain a federated strong learner with ideally improved robustness and predictive variance properties. Our experiments show that, in the considered practical scenario, CBMs provide equal or better results than FL, while being highly cost-effective. Our results demonstrate that the consensus paradigm may represent a valid alternative to FL for typical training tasks in medical imaging.