  Content-based histopathological image retrieval (CBHIR) has gained attention
in recent years, offering the capability to return histopathology images that
are content-wise similar to the query one from an established database.
However, in clinical practice, the continuously expanding size of WSI databases
limits the practical application of the current CBHIR methods. In this paper,
we propose a Lifelong Whole Slide Retrieval (LWSR) framework to address the
challenges of catastrophic forgetting by progressive model updating on
continuously growing retrieval database. Our framework aims to achieve the
balance between stability and plasticity during continuous learning. To
preserve system plasticity, we utilize local memory bank with reservoir
sampling method to save instances, which can comprehensively encompass the
feature spaces of both old and new tasks. Furthermore, A distance consistency
rehearsal (DCR) module is designed to ensure the retrieval queue's consistency
for previous tasks, which is regarded as stability within a lifelong CBHIR
system. We evaluated the proposed method on four public WSI datasets from TCGA
projects. The experimental results have demonstrated the proposed method is
effective and is superior to the state-of-the-art methods.
