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Hello World Deep Learning in Medical Imaging.

Paras Lakhani1, Daniel L Gray2, Carl R Pett2

  • 1Department of Radiology, Sidney Kimmel Jefferson Medical College, Thomas Jefferson University Hospital, Philadelphia, PA, 19107, USA. paras.lakhani@jefferson.edu.

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Summary

This tutorial introduces building deep neural networks for medical image classification. It provides code to help beginners start informatics projects using machine learning in medical imaging.

Keywords:
Artificial neural networksDeep learningMachine learningMedical imaging

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Area of Science:

  • Computer Science
  • Medical Imaging
  • Machine Learning

Background:

  • Machine learning, particularly deep learning, is increasingly popular for medical image analysis.
  • Deep learning models achieve state-of-the-art performance in image processing tasks.
  • The widespread adoption is facilitated by accessible machine learning frameworks and libraries.

Purpose of the Study:

  • To provide a high-level overview of constructing a deep neural network for medical image classification.
  • To offer practical code examples for researchers new to the field.
  • To facilitate the initiation of medical informatics projects involving deep learning.

Main Methods:

  • Overview of deep neural network architecture for image classification.
  • Demonstration of using machine learning frameworks and libraries.
  • Provision of accessible code for practical implementation.

Main Results:

  • A foundational understanding of deep learning for medical image classification.
  • Runnable code examples to accelerate project development.
  • Guidance for beginners entering the field of medical informatics.

Conclusions:

  • Deep learning offers powerful tools for medical image classification.
  • Accessible frameworks and code tutorials lower the barrier to entry for researchers.
  • This tutorial serves as a starting point for applying deep learning in medical informatics.