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Related Experiment Video

Updated: Aug 5, 2025

Rigid Embedding of Fixed and Stained, Whole, Millimeter-Scale Specimens for Section-free 3D Histology by Micro-Computed Tomography

Rigid Embedding of Fixed and Stained, Whole, Millimeter-Scale Specimens for Section-free 3D Histology by Micro-Computed Tomography

Published on: October 17, 2018

Deep texture representation analysis for histopathological images.

Ranny Rahaningrum Herdiantoputri¹, Daisuke Komura², Kei Fujisaka²

¹Department of Oral Pathology, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 13 1-5-45, Yushima, Bunkyo-ku, Tokyo 1138549, Japan.

|March 24, 2023

Summary

This summary is machine-generated.

Deep texture representations (DTRs) from bilinear CNNs objectively quantify tumor histopathology. This protocol simplifies DTR analysis for image retrieval and predictive modeling, enhancing cancer research.

Keywords:

Cancer Computer Sciences Microscopy

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Last Updated: Aug 5, 2025

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Published on: October 17, 2018

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Published on: July 26, 2014

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Area of Science:

Computational pathology
Digital pathology
Artificial intelligence in medicine

Background:

Deep texture representations (DTRs) offer objective quantification of histopathology images.
Bilinear convolutional neural networks (CNNs) are effective for generating DTRs.
DTRs have applications in image retrieval and supervised learning for medical analysis.

Purpose of the Study:

To present a simplified protocol for analyzing DTRs.
To demonstrate the workflow from data preparation to supervised learning model development.
To facilitate the prediction of histological profiles from DTRs.

Main Methods:

Utilizing bilinear CNNs to generate DTRs from histopathology images.
Implementing a workflow including data preparation and visualization.
Developing supervised learning models for profile prediction based on DTRs.
Incorporating content-based image retrieval (CBIR) for analysis.

Main Results:

Established a streamlined protocol for DTR analysis.
Enabled objective quantification and visualization of tumor histopathology.
Demonstrated the utility of DTRs in CBIR and predictive modeling.
Facilitated the prediction of histological profiles from image data.

Conclusions:

The simplified protocol effectively analyzes DTRs for tumor histopathology.
DTRs provide a powerful tool for objective image quantification and analysis.
This approach supports diverse applications, including image retrieval and predictive diagnostics.
The protocol aids in developing models to predict histological profiles, advancing cancer research.