[1]ZHOU Linlin,DING Weiping,ZHANG Wei,et al.Fuzzy attentional collaborative hashing network for medical image retrieval[J].CAAI Transactions on Intelligent Systems,2026,21(4):932-942.[doi:10.11992/tis.202508013]
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Fuzzy attentional collaborative hashing network for medical image retrieval

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 21 Number of periods: 2026 4 Page number: 932-942 Column: 学术论文—机器感知与模式识别 Public date: 2026-07-05
Title:
Fuzzy attentional collaborative hashing network for medical image retrieval
Author(s):
ZHOU Linlin;  DING Weiping;  ZHANG Wei;  HUANG Jiashuang;  WU Qiannan
School of Artificial Intelligence and Computer Science, Nantong University, Nantong 226019, China
Keywords:
medical image; image retrieval; fuzzy systems; attention mechanisms; deep learning; feature extraction; deep hashing; data processing
CLC:
TP391
DOI:
10.11992/tis.202508013
Abstract:
In recent years, innovations in medical imaging technology have driven a surge in image data, with efficient retrieval becoming a critical factor in enhancing diagnostic efficiency. Deep hashing algorithms are widely used in image retrieval due to their efficient encoding. However, these algorithms have limitations when applied to uncertain medical images and imbalanced datasets. To address these challenges, this paper proposes a fuzzy attention collaborative hashing network (FACH) for efficient medical image retrieval. First, fuzzy feature coordinator enhanced by attention is introduced, which improves inter-class discrimination by calculating attention weights for fuzzy features. Then, Transformer-based posterior collaborative encoder employs the multi-head self-attention mechanism to enhance image semantic modeling and capture the nonlinear relationships in medical images. Finally, the model is optimized by integrating pairwise loss, balance loss, quantization loss, and classification loss to generate compact and discriminative hash codes. Extensive experiments conducted on two medical image datasets validate the effectiveness of the proposed algorithm.
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