mlsanityData to 14 Oct 2026

Medical Image Retrieval Using Pretrained Embeddings

arXiv 2311.13547 ↗First posted 22 Nov 2023cs.CV cs.AIcited by 8

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Abstract (arXiv)

A wide range of imaging techniques and data formats available for medical images make accurate retrieval from image databases challenging. Efficient retrieval systems are crucial in advancing medical research, enabling large-scale studies and innovative diagnostic tools. Thus, addressing the challenges of medical image retrieval is essential for the continued enhancement of healthcare and research. In this study, we evaluated the feasibility of employing four state-of-the-art pretrained models for medical image retrieval at modality, body region, and organ levels and compared the results of two similarity indexing approaches. Since the employed networks take 2D images, we analyzed the impacts of weighting and sampling strategies to incorporate 3D information during retrieval of 3D volumes. We showed that medical image retrieval is feasible using pretrained networks without any additional training or fine-tuning steps. Using pretrained embeddings, we achieved a recall of 1 for various tasks at modality, body region, and organ level.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 18 of its 24 references that are in the map. First posted on arXiv 22 Nov 2023.

Builds on

18 of 24 references.

References · 18

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6 more
  1. 2023DreamSim: Learning New Dimensions of Human Visual Similarity using Synthetic Datacited by 337
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Cited by year

Citing papers per year: 2024: 5; 2025: 3.

8 papers.

  1. 2024Leveraging Foundation Models for Content-Based Image Retrieval in Radiologycited by 6
  2. 2025Towards All-in-One Medical Image Re-Identificationcited by 6
  3. 2024Content-Based Image Retrieval for Multi-Class Volumetric Radiology Images: A Benchmark Studycited by 4
  4. 2024Evaluating Pre-trained Convolutional Neural Networks and Foundation Models as Feature Extractors for Content-based Medical Image Retrievalcited by 4
  5. 2024ZenSVI: An Open-Source Software for the Integrated Acquisition, Processing and Analysis of Street View Imagery Towards Scalable Urban Sciencecited by 3
  6. 2025M3Ret: Unleashing Zero-shot Multimodal Medical Image Retrieval via Self-Supervisioncited by 2
  7. 2024Uncertainties of Latent Representations in Computer Visioncited by 1
  8. 2025Content-based 3D Image Retrieval and a ColBERT-inspired Re-ranking for Tumor Flagging and Stagingcited by 1