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Related Experiment Video

Updated: Jun 17, 2025

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Published on: October 27, 2023

Cross-modality sub-image retrieval using contrastive multimodal image representations.

Eva Breznik^1,2, Elisabeth Wetzer^3,4, Joakim Lindblad¹

¹Department of Information Technology, Uppsala University, 751 05, Uppsala, Sweden.

Scientific Reports

|August 13, 2024

Summary

We developed a novel content-based image retrieval (CBIR) system for cross-modality medical image search. This system effectively retrieves similar images across different imaging types, improving cancer diagnostics and tissue characterization.

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Area of Science:

Medical imaging analysis
Computational pathology
Biomedical informatics

Background:

Multimodal imaging is crucial for tissue characterization and cancer diagnostics.
Computational advances enable pattern discovery in large medical datasets.
Efficient and scalable image retrieval methods are needed for multimodal data.

Purpose of the Study:

To develop an application-independent content-based image retrieval (CBIR) system for cross-modality reverse (sub-)image search.
To improve the efficiency and reliability of retrieving similar images across different imaging modalities.

Main Methods:

Combined deep learning for modality-invariant representation generation with feature extraction and bag-of-words models.
Developed a system for cross-modality content-based image retrieval (CBIR).
Evaluated the system using a dataset of brightfield and second harmonic generation microscopy images.

Main Results:

The proposed CBIR system demonstrated superior performance compared to existing methods for cross-modality (sub-)image retrieval.
The study highlighted the importance of equivariance and invariance properties in learned representations and feature extractors.
The system effectively embeds different imaging modalities into a common representational space.

Conclusions:

The developed CBIR system offers an efficient and reliable solution for cross-modality image retrieval in medical applications.
The findings support the use of deep learning and robust feature extraction for advancing multimodal medical image analysis.
This approach has the potential to enhance cancer diagnostics and tissue characterization through improved image search capabilities.