SDH方法在32位哈希下实现医疗图像检索最优性能。
Comparative Analysis of Binarization Methods For Medical Image Hashing On Odir Dataset
- 采用SDH方法进行医学图像哈希，使用32位编码
- mAP@100达0.9184，优于其他三种方法
- 适合低存储高效率的医疗图像检索场景
本研究在ODIR数据集上评估了四种二值化方法：局部敏感哈希（LSH）、迭代量化（ITQ）、基于核的监督哈希（KSH）和监督离散哈希（SDH），均基于深度特征嵌入。实验结果表明，SDH表现最佳，仅用32位代码即达到mAP@100为0.9184，优于LSH、ITQ和KSH。相较于已有研究，本方法具有显著优势：Fang等人在Fundus-iSee（48位）和ASOCT-Cataract（48位）上分别报告0.7528和0.8856；Wijesinghe等人在KVASIR上以256位获得94.01。尽管比特数大幅减少，本方法仍接近当前最优水平。结果证明SDH是所测试方法中最有效的，兼具精度、存储与效率，适用于医疗图像检索与设备管理。
原文摘要 · Abstract (English)
In this study, we evaluated four binarization methods. Locality-Sensitive Hashing (LSH), Iterative Quantization (ITQ), Kernel-based Supervised Hashing (KSH), and Supervised Discrete Hashing (SDH) on the ODIR dataset using deep feature embeddings. Experimental results show that SDH achieved the best performance, with an mAP@100 of 0.9184 using only 32-bit codes, outperforming LSH, ITQ, and KSH. Compared with prior studies, our method proved highly competitive: Fang et al. reported 0.7528 (Fundus-iSee, 48 bits) and 0.8856 (ASOCT-Cataract, 48 bits), while Wijesinghe et al. achieved 94.01 (KVASIR, 256 bits). Despite using significantly fewer bits, our SDH-based framework reached retrieval accuracy close to the state-of-the-art. These findings demonstrate that SDH is the most effective approach among those tested, offering a practical balance of accuracy, storage, and efficiency for medical image retrieval and device inventory management.
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