Graph Enhanced Hashing Networks for Cross-Modal Patient Retrieval

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Key Points

This research aims to improve cross-modal patient data retrieval by incorporating graph-enhanced hashing networks.
Developed a label encoder using co-occurrence relationships to construct a label graph.
Introduced a cross-modal attention fusion mechanism to enhance embedding representations.
Designed a similarity loss function to optimize both intra- and inter-modal relationships.
In the chest X-ray and diagnostic report dataset MIMIC-CXR, the proposed model outperformed three classical and four advanced hashing methods.
Achieved average precision improvements of 2.98%, 1.21%, 0.63%, and 0.53% at hash lengths of 16, 32, 64, and 128 bits respectively.
In the inverse task of retrieving X-ray images from reports, average precision improvements of 0.91%, 0.75%, 1.03%, and 0.57% were observed.

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Demonstrates improved diagnostic retrieval using enhanced hashing in clinical settings, suggesting better patient analysis.

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Gu et al. (2024) studied this question.

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