Yang Erkun, Deng Cheng, Liu Mingxia
Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
Xidian University, Xi'an, China.
Mach Learn Med Imaging. 2023 Oct;14349:396-406. doi: 10.1007/978-3-031-45676-3_40. Epub 2023 Oct 15.
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 (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 the representation compact quantization within a novel Bayesian learning framework, where a proxy embedding-based likelihood function is developed to alleviate the sampling issue for traditional similarity supervision. Additionally, a Gaussian prior is employed to reduce the quantization losses. By utilizing pre-computed lookup tables, the proposed DBQ can enable efficient and effective similarity search. Extensive experiments conducted on 2, 008 structural MRI scans from three benchmark neuroimage datasets demonstrate that our method outperforms previous state-of-the-arts.
神经图像检索在为医生提供访问以前类似病例的途径方面起着至关重要的作用，这对于基于案例的推理和循证医学至关重要。由于计算和存储成本较低，基于哈希的搜索技术已被广泛用于建立图像检索系统。然而，这些方法往往存在不可忽视的量化损失，这可能会降低整体搜索性能。为了解决这个问题，本文提出了一种紧凑编码解决方案，即深度贝叶斯量化（DBQ），它专注于深度紧凑量化，可以估计连续的神经图像表示，并在现有哈希解决方案上实现卓越性能。具体而言，DBQ在一个新颖的贝叶斯学习框架内无缝结合了深度表示学习和表示紧凑量化，其中开发了一种基于代理嵌入的似然函数来缓解传统相似性监督的采样问题。此外，采用高斯先验来减少量化损失。通过利用预先计算的查找表，所提出的DBQ可以实现高效且有效的相似性搜索。在来自三个基准神经图像数据集的2008次结构MRI扫描上进行的大量实验表明，我们的方法优于以前的先进方法。
