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Deep learning workflow in radiology: a primer.

Emmanuel Montagnon1, Milena Cerny1, Alexandre Cadrin-Chênevert2

  • 1Centre de recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Québec, Canada.

Insights Into Imaging
|February 11, 2020
PubMed
Summary
This summary is machine-generated.

This guide offers practical steps for deep learning projects in radiology, covering use cases, team building, and data selection. It addresses challenges for successful implementation in medical imaging analysis.

Keywords:
CohortingConvolutional neural networkDeep learningMedical imagingReview article

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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Deep learning shows high performance in radiology for tasks like detection, segmentation, and classification.
  • There's a growing interest in applying deep learning to medical imaging analysis.

Purpose of the Study:

  • To provide practical, step-by-step guidance for deep learning projects in radiology.
  • To outline clinical use cases, team composition, and selection criteria for patients, data, models, and hardware.
  • To illustrate the workflow using a colorectal liver metastasis imaging example.

Main Methods:

  • Overview of clinical use cases for deep learning in radiology.
  • Description of multi-disciplinary team composition.
  • Summary of approaches for patient, data, model, and hardware selection.
  • Illustration with a prototypical project on colorectal liver metastasis imaging.

Main Results:

  • Demonstrates a workflow for liver lesion detection, segmentation, classification, monitoring, and prediction of tumor recurrence and survival.
  • Highlights challenges including ethical considerations, data collection, anonymization, and annotation availability.

Conclusions:

  • The article provides adaptable practical guidance for deep learning projects in radiology.
  • It emphasizes the importance of a structured approach for automated medical image analysis.