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Leveraging foundation models for content-based image retrieval in radiology.

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  • 1German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Heidelberg, Germany; Faculty of Mathematics and Computer Science, Heidelberg University, Heidelberg, Germany.

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Vision foundation models offer versatile, off-the-shelf feature extraction for content-based image retrieval (CBIR) in radiology. These models achieve performance comparable to specialized systems without specific tuning, enhancing diagnostic aid and medical research.

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Content-based image retrievalFoundation modelsMedical imagingSelf-supervised learningSupervised learningWeakly-supervised learning

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Area of Science:

  • Radiology and Medical Imaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • Content-based image retrieval (CBIR) systems in radiology are often specialized for specific pathologies, limiting their broad applicability.
  • Existing CBIR systems require extensive training and tuning for optimal performance.
  • Vision foundation models (VFMs) demonstrate potential for generating general-purpose visual features applicable across diverse tasks.

Purpose of the Study:

  • To investigate the efficacy of using VFMs as off-the-shelf feature extractors for CBIR in radiology.
  • To benchmark various VFMs on a large-scale, multi-modal radiological image dataset.
  • To evaluate the potential of VFMs to create versatile, general-purpose medical image retrieval systems.

Main Methods:

  • Benchmarking a diverse set of VFMs on a dataset of 1.6 million 2D radiological images across four modalities and 161 pathologies.
  • Evaluating retrieval performance using metrics such as Precision at K (P@k).
  • Analyzing the impact of index size, embedding space quality, and retrieval of anatomical versus pathological structures.

Main Results:

  • Weakly-supervised VFMs, particularly BiomedCLIP, demonstrated high effectiveness, achieving a P@1 of up to 0.594 without additional training.
  • Performance was comparable to specialized CBIR systems, highlighting the versatility of VFMs.
  • Analysis revealed insights into index size impact, embedding space characteristics, and challenges in retrieving specific image features.

Conclusions:

  • VFMs show significant potential for advancing CBIR in radiology, offering a versatile alternative to specialized systems.
  • The proposed approach enables general-purpose medical image retrieval without the need for specific model tuning.
  • This research paves the way for more adaptable and powerful diagnostic aid and medical research tools in radiology.