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ROCOv2: Radiology Objects in COntext Version 2, an Updated Multimodal Image Dataset.

Johannes Rückert1, Louise Bloch1,2,3, Raphael Brüngel1,2,3

  • 1Department of Computer Science, University of Applied Sciences and Arts Dortmund, Dortmund, Germany.

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|June 26, 2024
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This summary is machine-generated.

This study introduces ROCOv2, a large multimodal dataset of 79,789 radiological images with medical concepts and captions. It enhances automated medical image analysis by providing valuable training data for annotation and classification models.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • Automated medical image analysis requires extensive, high-quality labeled training data, which is challenging to produce.
  • Existing datasets may lack the scale or multimodal annotations needed for advanced deep learning models.

Purpose of the Study:

  • Introduce Radiology Object in COntext version 2 (ROCOv2), an updated and expanded multimodal dataset for medical imaging.
  • Facilitate the development and evaluation of automated medical image analysis systems, particularly for concept detection and caption prediction.
  • Provide a comprehensive resource for training image annotation models, multi-label image classification, and pre-training medical domain models.

Main Methods:

  • Compiled ROCOv2 by updating the original ROCO dataset with 35,705 new radiological images from the PMC Open Access subset.
  • Integrated associated medical concepts, including manually curated imaging modalities, anatomical, and directional concepts for X-rays.
  • Extracted medical concepts and captions from the PMC Open Access subset, leveraging the Unified Medical Language System (UMLS).

Main Results:

  • ROCOv2 comprises 79,789 radiological images with associated medical concepts and captions.
  • The dataset includes enhanced annotations for imaging modalities, anatomy, and directionality, particularly for X-rays.
  • ROCOv2 has been successfully utilized in ImageCLEFmedical Caption 2023 for concept detection and caption prediction tasks.

Conclusions:

  • ROCOv2 offers a valuable, large-scale multimodal dataset for advancing automated medical image analysis.
  • The dataset supports diverse applications, including image annotation, multi-label classification, and pre-training of deep learning models.
  • ROCOv2 is suitable for evaluating deep learning models in medical imaging, especially for multi-task learning scenarios.