Deep Bayesian Quantization for Supervised Neuroimage Search.

ABSTRACT: Neuroimage retrieval plays a crucial role in providing physicians with access to previous similar cases, which is essential for case-based reasoning and evidence-based medicine. Due to low computation and storage costs, hashing-based search techniques have been widely adopted for establishing image retrieval systems. However, these methods often suffer from nonnegligible quantization loss, which can degrade the overall search performance. To address this issue, this paper presents a compact coding solution namely Deep Bayesian Quantization (DBQ), which focuses on deep compact quantization that can estimate continuous neuroimage representations and achieve superior performance over existing hashing solutions. Specifically, DBQ seamlessly combines the deep representation learning and

SUBMITTER: Yang E 

PROVIDER: S-EPMC10883338 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature