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@ARTICLE{Denner:294878,
      author       = {S. Denner$^*$ and D. Zimmerer$^*$ and D. Bounias$^*$ and M.
                      Bujotzek$^*$ and S. Xiao$^*$ and L. Kausch$^*$ and P.
                      Schader$^*$ and T. Penzkofer and P. F. Jäger$^*$ and K.
                      Maier-Hein$^*$},
      title        = {{L}everaging {F}oundation {M}odels for {C}ontent-{B}ased
                      {M}edical {I}mage {R}etrieval in {R}adiology},
      publisher    = {arXiv},
      reportid     = {DKFZ-2024-02588},
      year         = {2024},
      abstract     = {Content-based image retrieval (CBIR) has the potential to
                      significantly improve diagnostic aid and medical research in
                      radiology. Current CBIR systems face limitations due to
                      their specialization to certain pathologies, limiting their
                      utility. In response, we propose using vision foundation
                      models as powerful and versatile off-the-shelf feature
                      extractors for content-based medical image retrieval. By
                      benchmarking these models on a comprehensive dataset of 1.6
                      million 2D radiological images spanning four modalities and
                      161 pathologies, we identify weakly-supervised models as
                      superior, achieving a P@1 of up to 0.594. This performance
                      not only competes with a specialized model but does so
                      without the need for fine-tuning. Our analysis further
                      explores the challenges in retrieving pathological versus
                      anatomical structures, indicating that accurate retrieval of
                      pathological features presents greater difficulty. Despite
                      these challenges, our research underscores the vast
                      potential of foundation models for CBIR in radiology,
                      proposing a shift towards versatile, general-purpose medical
                      image retrieval systems that do not require specific
                      tuning.},
      keywords     = {Computer Vision and Pattern Recognition (cs.CV) (Other) /
                      Information Retrieval (cs.IR) (Other) / FOS: Computer and
                      information sciences (Other)},
      cin          = {E230 / E290},
      cid          = {I:(DE-He78)E230-20160331 / I:(DE-He78)E290-20160331},
      pnm          = {315 - Bildgebung und Radioonkologie (POF4-315)},
      pid          = {G:(DE-HGF)POF4-315},
      typ          = {PUB:(DE-HGF)25},
      doi          = {10.48550/ARXIV.2403.06567},
      url          = {https://inrepo02.dkfz.de/record/294878},
}