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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

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Intelligent Imaging: Developing a Machine Learning Project.

Geoffrey M Currie¹

¹School of Dentistry and Health Sciences, Charles Sturt University, Wagga Wagga, Australia; Department of Radiology, Baylor College of Medicine, Houston, Texas gcurrie@csu.edu.au.

Journal of Nuclear Medicine Technology

|December 28, 2020

Summary

This summary is machine-generated.

Start with smaller machine learning (ML) projects using artificial neural networks before advancing to deep learning (DL) with convolutional neural networks. This approach builds an AI footprint effectively for immediate impact and future complex problem-solving.

Keywords:

artificial intelligence artificial neural network machine learning nuclear medicine

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Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Rapid advancements in Artificial Intelligence (AI) present numerous opportunities for significant projects.
A structured approach is needed to effectively integrate AI into practice and research.
Existing AI frameworks offer potential for innovation but require a strategic implementation plan.

Purpose of the Study:

To outline a sensible and sustainable strategy for developing an institutional Artificial Intelligence (AI) program.
To propose a phased approach, starting with simpler Machine Learning (ML) initiatives before progressing to Deep Learning (DL).
To provide examples of entry-level ML projects and inspire innovative problem-solving using AI.

Main Methods:

Utilizing artificial neural networks for initial Machine Learning (ML) based projects.
Gradually progressing to more complex Deep Learning (DL) approaches, specifically convolutional neural networks.
Developing and presenting mock projects and strategies for practical AI implementation.

Main Results:

Simple, resource-light ML approaches are identified as ideal starting points for problem-solving.
These foundational ML projects offer accessible entry points for institutional AI program development.
The proposed strategy enables solutions with significant and immediate practical impact.

Conclusions:

A logical progression from ML to DL, starting with simpler models, is recommended for AI development.
ML can be used to analyze problems and identify those most suitable for advanced DL techniques.
This phased strategy facilitates effective AI adoption and maximizes immediate benefits.