Federated Learning for Enhanced Cell Nuclei Segmentation in Histopathological Images

This study addresses the critical challenge of cell nuclei segmentation in histopathological image analysis, which is essential for cancer diagnosis and prognosis. Traditional segmentation methods often struggle with complexities such as overlapping nuclei and variations in shape and size. Recent advancements in machine learning and deep learning, particularly convolutional neural networks, have improved segmentation accuracy but face limitations due to the need for large annotated datasets, often constrained by privacy regulations in the medical field. This research explores federated learning as a solution, enabling collaborative model training across institutions without sharing sensitive patient data. By leveraging diverse datasets while maintaining privacy, federated learning enhances model robustness and generalization capabilities. The study evaluates various federated learning techniques on in- and out-of-domain test sets, aiming to improve the reliability of segmentation models in clinical settings. The findings suggest that federated learning techniques can effectively address the challenges of data scarcity and domain shift, paving the way for more accurate and widely applicable segmentation algorithms in computational pathology.