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A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
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Updated: Jun 17, 2026

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

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Published on: August 16, 2020

Machine learning in computational histopathology: Challenges and opportunities.

Michael Cooper^1,2,3, Zongliang Ji^1,3, Rahul G Krishnan^1,3,4

¹Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.

Genes, Chromosomes & Cancer

|June 14, 2023

Summary

This summary is machine-generated.

Machine learning and deep learning are revolutionizing cancer diagnosis using digital histopathology images. These computational tools automate risk prediction and stratification, improving oncology workflows.

Keywords:

computational pathology deep learning machine learning

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Area of Science:

Computational pathology
Digital pathology
Machine learning in oncology

Background:

Histopathological images are crucial for cancer diagnosis and staging.
Digitization has shifted analysis from microscopes to computers.
Machine learning (ML) and deep learning (DL) have emerged as powerful analytical tools.

Purpose of the Study:

To review the rise of computational models in histopathology.
To highlight clinical tasks automated by ML/DL.
To discuss ML techniques and future opportunities in the field.

Main Methods:

Review of machine learning and deep learning applications in computational histopathology.
Analysis of automated prediction and risk stratification models.
Discussion of various ML techniques applied to digitized histopathology slides.

Main Results:

ML models trained on large datasets show success in automated prediction and risk stratification.
Significant advancements in automating clinical tasks within oncology workflows.
Emergence of computational tools for analyzing digitized histopathology images.

Conclusions:

Machine learning and deep learning are transforming cancer diagnosis through computational histopathology.
These technologies offer opportunities for improved patient risk stratification and automated clinical decision-making.
Further research into ML techniques can address open problems and enhance future applications.