  Computer-aided diagnosis (CAD) can help pathologists improve diagnostic
accuracy together with consistency and repeatability for cancers. However, the
CAD models trained with the histopathological images only from a single center
(hospital) generally suffer from the generalization problem due to the
straining inconsistencies among different centers. In this work, we propose a
pseudo-data based self-supervised federated learning (FL) framework, named
SSL-FT-BT, to improve both the diagnostic accuracy and generalization of CAD
models. Specifically, the pseudo histopathological images are generated from
each center, which contains inherent and specific properties corresponding to
the real images in this center, but does not include the privacy information.
These pseudo images are then shared in the central server for self-supervised
learning (SSL). A multi-task SSL is then designed to fully learn both the
center-specific information and common inherent representation according to the
data characteristics. Moreover, a novel Barlow Twins based FL (FL-BT) algorithm
is proposed to improve the local training for the CAD model in each center by
conducting contrastive learning, which benefits the optimization of the global
model in the FL procedure. The experimental results on three public
histopathological image datasets indicate the effectiveness of the proposed
SSL-FL-BT on both diagnostic accuracy and generalization.
