Lifelong Histopathology Whole Slide Image Retrieval via Distance Consistency Rehearsal
This addresses the problem of continuously expanding histopathology image databases for clinical practitioners, representing an incremental improvement in lifelong learning for medical imaging.
The paper tackles catastrophic forgetting in lifelong whole slide image retrieval by proposing a framework with a distance consistency rehearsal module, achieving superior performance to state-of-the-art methods on four public WSI datasets.
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.