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. 2024 Jul 2;11(7):673.
doi: 10.3390/bioengineering11070673.

Deep Attention Fusion Hashing (DAFH) Model for Medical Image Retrieval

Gangao Wu  1   2   3 , Enhui Jin  1   2   3 , Yanling Sun  1   2   4 , Bixia Tang  1   2   4 , Wenming Zhao  1   2   3   4
Affiliations

Deep Attention Fusion Hashing (DAFH) Model for Medical Image Retrieval

Gangao Wu et al. Bioengineering (Basel). .

Abstract

In medical image retrieval, accurately retrieving relevant images significantly impacts clinical decision making and diagnostics. Traditional image-retrieval systems primarily rely on single-dimensional image data, while current deep-hashing methods are capable of learning complex feature representations. However, retrieval accuracy and efficiency are hindered by diverse modalities and limited sample sizes.

Objective: To address this, we propose a novel deep learning-based hashing model, the Deep Attention Fusion Hashing (DAFH) model, which integrates advanced attention mechanisms with medical imaging data.

Methods: The DAFH model enhances retrieval performance by integrating multi-modality medical imaging data and employing attention mechanisms to optimize the feature extraction process. Utilizing multimodal medical image data from the Cancer Imaging Archive (TCIA), this study constructed and trained a deep hashing network that achieves high-precision classification of various cancer types.

Results: At hash code lengths of 16, 32, and 48 bits, the model respectively attained Mean Average Precision (MAP@10) values of 0.711, 0.754, and 0.762, highlighting the potential and advantage of the DAFH model in medical image retrieval.

Conclusions: The DAFH model demonstrates significant improvements in the efficiency and accuracy of medical image retrieval, proving to be a valuable tool in clinical settings.

Keywords: deep hashing; deep learning; medical image retrieval.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Flow chart for data preprocessing.
Figure 2
Figure 2
The architecture of DAFH.
Figure 3
Figure 3
The confusion matrix of MAP@1.
See this image and copyright information in PMC

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