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Proactive Construction of an Annotated Imaging Database for Artificial Intelligence Training.

Caroline Bivik Stadler1,2, Martin Lindvall3,4, Claes Lundström5,3,4

  • 1Center for Medical Image Science and Visualization (CMIV), Linköping University Hospital, Linköping University, SE-581 85, Linköping, Sweden. caroline.bivik.stadler@liu.se.

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|November 10, 2020
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Summary
This summary is machine-generated.

Developing clinical-grade artificial intelligence (AI) requires extensive, high-quality annotated medical imaging data. This project created a detailed oncology imaging database and identified key principles for scalable AI training data construction.

Keywords:
AnnotationArtificial intelligenceCase collectionPathologyRadiology

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Area of Science:

  • Medical Imaging Informatics
  • Artificial Intelligence in Healthcare
  • Oncology Diagnostics

Background:

  • Artificial intelligence (AI) shows significant potential for enhancing medical imaging diagnostics.
  • A major limitation in developing clinical-grade AI is the scarcity of large, high-quality annotated training datasets.
  • High-quality ground truth annotation is crucial for effective AI model development in medical imaging.

Purpose of the Study:

  • To establish and detail the construction of a comprehensive database of annotated oncology imaging data.
  • To support diverse AI training tasks including detection, quantification, segmentation, and classification.
  • To focus on annotation quality and generality for broad AI applicability.

Main Methods:

  • Compilation of a labeled image dataset encompassing oncology imaging from pathology and radiology.
  • Inclusion of data from multiple cancer types: breast, ovary, skin, colon, skeleton, and liver.
  • Exploration of best practices for scalable, high-quality medical image collection.

Main Results:

  • Successful creation of a detailed, annotated oncology imaging database.
  • The database contains labeled image data across six distinct anatomical regions and cancer types.
  • Identification of generic lessons learned for constructing medical imaging databases for AI training.

Conclusions:

  • The developed database serves as a valuable resource for AI model development in oncology.
  • Eight guiding principles for organizing efforts in medical imaging database construction were summarized.
  • This work provides a framework for scalable, high-quality medical image data collection for AI.