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| 001 | 294878 | ||
| 005 | 20241210182915.0 | ||
| 024 | 7 | _ | |a 10.48550/ARXIV.2403.06567 |2 doi |
| 037 | _ | _ | |a DKFZ-2024-02588 |
| 100 | 1 | _ | |a Denner, Stefan |0 P:(DE-He78)e35f6a9bd89b1c66d107df8a2325a758 |b 0 |e First author |u dkfz |
| 245 | _ | _ | |a Leveraging Foundation Models for Content-Based Medical Image Retrieval in Radiology |
| 260 | _ | _ | |c 2024 |b arXiv |
| 336 | 7 | _ | |a Preprint |b preprint |m preprint |0 PUB:(DE-HGF)25 |s 1733825110_7696 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a WORKING_PAPER |2 ORCID |
| 336 | 7 | _ | |a Electronic Article |0 28 |2 EndNote |
| 336 | 7 | _ | |a preprint |2 DRIVER |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a Output Types/Working Paper |2 DataCite |
| 520 | _ | _ | |a 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. |
| 536 | _ | _ | |a 315 - Bildgebung und Radioonkologie (POF4-315) |0 G:(DE-HGF)POF4-315 |c POF4-315 |f POF IV |x 0 |
| 588 | _ | _ | |a Dataset connected to DataCite |
| 650 | _ | 7 | |a Computer Vision and Pattern Recognition (cs.CV) |2 Other |
| 650 | _ | 7 | |a Information Retrieval (cs.IR) |2 Other |
| 650 | _ | 7 | |a FOS: Computer and information sciences |2 Other |
| 700 | 1 | _ | |a Zimmerer, David |0 P:(DE-He78)c1fcef80eab3d1e4fc187faece1a439c |b 1 |u dkfz |
| 700 | 1 | _ | |a Bounias, Dimitrios |0 P:(DE-He78)95f361c74f433d336bfd0a95bc9b0eba |b 2 |u dkfz |
| 700 | 1 | _ | |a Bujotzek, Markus |0 P:(DE-He78)d52d4217d38d20b78d1bc8014e2b0c35 |b 3 |u dkfz |
| 700 | 1 | _ | |a Xiao, Shuhan |0 P:(DE-He78)d2bf7126723ea8f6005ba141ea3c3e2c |b 4 |u dkfz |
| 700 | 1 | _ | |a Kausch, Lisa |0 P:(DE-He78)4854a5d7f6e812324fd74320396f4178 |b 5 |
| 700 | 1 | _ | |a Schader, Philipp |0 P:(DE-He78)2529b97355581f2d933fcfd7908d9ed4 |b 6 |u dkfz |
| 700 | 1 | _ | |a Penzkofer, Tobias |b 7 |
| 700 | 1 | _ | |a Jäger, Paul F. |0 P:(DE-He78)04a0b5a49db132d8f00cee326cb743ca |b 8 |u dkfz |
| 700 | 1 | _ | |a Maier-Hein, Klaus |0 P:(DE-He78)33c74005e1ce56f7025c4f6be15321b3 |b 9 |e Last author |u dkfz |
| 773 | _ | _ | |a 10.48550/ARXIV.2403.06567 |
| 909 | C | O | |o oai:inrepo02.dkfz.de:294878 |p VDB |
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| 914 | 1 | _ | |y 2024 |
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| 980 | _ | _ | |a preprint |
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