Content-Based 3D Image Retrieval and a ColBERT-Inspired Re-ranking for Tumor Flagging and Staging.
The increasing volume of medical images poses challenges for radiologists in retrieving relevant cases. Content-based image retrieval (CBIR) systems offer potential for efficient access to similar cases, yet lack standardized evaluation and comprehensive studies. Building on prior studies for tumor...
| Publicado en: | Journal of Imaging Informatics in Medicine Vol. 39; no. 3; pp. 2072 - 2095 |
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| Autores principales: | , , |
| Formato: | diagnostic images equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=194225512&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 194225512 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Jun2026 vid: 39 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 194225512 189894372 194225512 194225512 10.1007/s10278-025-01598-0 194225512 ppf: 2072 ppct: 23 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Content-Based 3D Image Retrieval and a ColBERT-Inspired Re-ranking for Tumor Flagging and Staging. aug: au: Khun Jush, Farnaz Vogler, Steffen Lenga, Matthias affil: https://ror.org/04hmn8g73 Radiology R&D, Bayer AG, Müllerstr. 178, 13353, Berlin, Germany sug: subj: Neoplasm Staging Imaging, Three-Dimensional Image Retrieval Systems Neoplasms Pathology Image Interpretation, Computer Assisted Human Wilcoxon Signed Rank Test Descriptive Statistics Motivation Colonic Neoplasms Lung Neoplasms Algorithms Natural Language Processing ab: The increasing volume of medical images poses challenges for radiologists in retrieving relevant cases. Content-based image retrieval (CBIR) systems offer potential for efficient access to similar cases, yet lack standardized evaluation and comprehensive studies. Building on prior studies for tumor characterization via CBIR, this study advances CBIR research for volumetric medical images through three key contributions: (1) a framework eliminating reliance on pre-segmented data and organ-specific datasets, aligning with large and unstructured image archiving systems, i.e., PACS in clinical practice; (2) introduction of C-MIR, a novel volumetric re-ranking method adapting ColBERT's contextualized late interaction mechanism for 3D medical imaging; and (3) comprehensive evaluation across four tumor sites using three feature extractors and three database configurations. Our evaluations highlight the significant advantages of C-MIR. We demonstrate the successful adaptation of the late interaction principle to volumetric medical images, enabling effective context-aware re-ranking. A key finding is C-MIR's ability to effectively localize the region of interest, eliminating the need for pre-segmentation of datasets and offering a computationally efficient alternative to systems relying on expensive data enrichment steps. C-MIR demonstrates promising improvements in tumor flagging, achieving improved performance, particularly for colon and lung tumors ( p < 0.05 ). C-MIR also shows potential for improving tumor staging, warranting further exploration of its capabilities. Ultimately, our work seeks to bridge the gap between advanced retrieval techniques and their practical applications in healthcare, paving the way for improved diagnostic processes. pubtype: Academic Journal doctype: diagnostic images equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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