Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: May 25, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

An algorithm for cancer nest feature extraction from pathological images.

Tomoyuki Hiroyasu1, Hiroaki Yamaguchi, Sosuke Fujita

  • 1Department of Life and Medical Sciences, Doshisha University.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|January 19, 2012
PubMed
Summary

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Automating region selection with genetic algorithms for energy landscape analyses of brain dynamics.

Patterns (New York, N.Y.)·2026
Same author

Cellular effects induced by insoluble radioactive cesium-bearing microparticles derived from the Fukushima Daiichi nuclear power plant accident.

International journal of radiation biology·2026
Same author

Deciphering mechanical determinants of morphological evolution.

Cell·2026
Same author

Clinically informed intermediate reasoning enables generalizable prostate cancer prognostication through machine learning in limited settings.

NPJ digital medicine·2025
Same author

Task-guided generative adversarial networks for synthesizing and augmenting structural connectivity matrices for connectivity-based prediction.

Network neuroscience (Cambridge, Mass.)·2025
Same author

[Bayesian Regression Modeling of the Correlation between Post-Progression Survival and Overall Survival in Immune Checkpoint Inhibitor Therapy].

Gan to kagaku ryoho. Cancer & chemotherapy·2025

This study introduces an automated algorithm for creating filters to identify cancer regions in pathological images. The method successfully extracts key features like area and circularity, aiding cancer diagnosis.

Area of Science:

  • Digital Pathology
  • Medical Image Analysis
  • Computational Pathology

Background:

  • Accurate identification of cancerous regions in pathological images is crucial for diagnosis.
  • Manual analysis can be time-consuming and prone to inter-observer variability.
  • Automated methods are needed to improve efficiency and consistency in cancer image analysis.

Purpose of the Study:

  • To develop an automated algorithm for generating extraction filters for affected regions in cancer images.
  • To extract quantitative features from cancer nests to support pathological diagnosis.
  • To evaluate the effectiveness and versatility of the developed filters.

Main Methods:

  • The proposed algorithm involves two main steps: extraction of affected region candidates and false positive elimination.

Related Experiment Videos

Last Updated: May 25, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

  • Automatic Construction of Tree-structural Image Transformation (ACTIT) was employed to construct the extraction filters.
  • The algorithm was applied to mouth cancer pathological images to derive filters and extract features.

    • Main Results:

    • The algorithm successfully generated filters capable of extracting cancer nests from pathological images.
    • Quantitative features, including area and degree of circularity of cancer nests, were automatically derived.
    • The generated filters demonstrated versatility when applied to different images from the same specimen.

    Conclusions:

    • The proposed automated algorithm effectively obtains filters for extracting cancer nests from pathological images.
    • The derived filters and extracted features can significantly support pathological diagnosis.
    • The algorithm shows promise for general application in cancer image analysis.