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

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

Published on: August 16, 2020

A primer for understanding radiology articles about machine learning and deep learning.

Takeshi Nakaura¹, Toru Higaki², Kazuo Awai²

¹Department of Diagnostic Radiology, Graduate school of medical sciences, Japan Kumamoto university, 1-1-1 Honjo, Chuo-ku, 860-8556 Kumamoto City, Japan.

Diagnostic and Interventional Imaging

|October 30, 2020

Summary

Machine learning and deep learning are increasingly used in medical imaging. This article explains the core concepts of machine learning, radiomics, and deep learning for clinicians.

Keywords:

Deep learning Machine learning Magnetic resonance imaging Tomography,X-ray computed

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

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Area of Science:

Medical Imaging
Artificial Intelligence
Computational Pathology

Background:

Machine learning and deep learning applications are expanding rapidly within medical imaging.
Existing expert commentaries on these topics are often too technical for clinicians.
There is a need for accessible explanations of these advanced techniques for medical professionals.

Purpose of the Study:

To demystify machine learning, radiomics, and deep learning for a clinical audience.
To provide a foundational understanding of AI in medical imaging.
To bridge the knowledge gap between AI experts and clinicians.

Main Methods:

Conceptual explanation of machine learning principles.
Introduction to radiomics and its relevance in imaging.
Overview of deep learning methodologies in the context of medical imaging.

Main Results:

Key concepts of machine learning, radiomics, and deep learning are presented.
The fundamental principles are explained in a manner accessible to clinicians.
Familiarity with these AI techniques is enhanced for medical professionals.

Conclusions:

Machine learning, radiomics, and deep learning are becoming integral to medical imaging.
Understanding these AI concepts is achievable for clinicians.
This work aims to facilitate the adoption and application of AI in clinical practice.