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Multimodal Optical Imaging Platform for Studying Cellular Metabolism

Multimodal Optical Imaging Platform for Studying Cellular Metabolism

Published on: June 6, 2025

Histology image search using multimodal fusion.

Juan C Caicedo¹, Jorge A Vanegas², Fabian Páez²

¹University of Illinois at Urbana-Champaign, Sieble Center for Computer Science, 201 N Goodwin Ave, Urbana, IL 61801, USA.

Journal of Biomedical Informatics

|May 14, 2014

Summary

This study introduces a new histology image indexing method using combined visual and semantic data. This approach enhances image retrieval by creating a fused representation, outperforming traditional methods.

Keywords:

Digital pathology Histology Image search Multimodal fusion Semantic spaces Visual representation

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Multimodal Hierarchical Imaging of Serial Sections for Finding Specific Cellular Targets within Large Volumes

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Published on: March 20, 2018

Area of Science:

Digital Pathology
Medical Informatics
Computer Vision

Background:

Histology image analysis often relies on either visual features or semantic annotations, each with limitations.
Integrating these complementary data sources is crucial for robust image retrieval systems.

Purpose of the Study:

To develop a novel histology image indexing strategy leveraging multimodal representations.
To create a fused image representation combining visual features and semantic annotations for improved image search.

Main Methods:

A strategy to build a fused image representation using matrix factorization and data reconstruction principles.
Generation of a set of multimodal features that can recover representations without semantic annotations.
Indexing new images using visual features only and accepting single example images as queries.

Main Results:

Experimental evaluations on three histology image datasets demonstrate the effectiveness of the proposed strategy.
The method successfully creates multimodal representations for histology image search.
Outperforms the popular late fusion approach in combining information for image retrieval.

Conclusions:

The proposed strategy offers a simple yet effective approach for multimodal histology image representation.
Enables flexible image indexing and retrieval, even with limited or missing data modalities.
Represents a significant advancement in histology image search and analysis capabilities.