English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The increasing use of medical imaging in healthcare settings presents asignificant challenge due to the increasing workload for radiologists, yet italso offers opportunity for enhancing healthcare outcomes if effectivelyleveraged. 3D image retrieval holds potential to reduce radiologist workloadsby enabling clinicians to efficiently search through diagnostically similar orotherwise relevant cases, resulting in faster and more precise diagnoses.However, the field of 3D medical image retrieval is still emerging, lackingestablished evaluation benchmarks, comprehensive datasets, and thoroughstudies. This paper attempts to bridge this gap by introducing a novelbenchmark for 3D Medical Image Retrieval (3D-MIR) that encompasses fourdifferent anatomies imaged with computed tomography. Using this benchmark, weexplore a diverse set of search strategies that use aggregated 2D slices, 3Dvolumes, and multi-modal embeddings from popular multi-modal foundation modelsas queries. Quantitative and qualitative assessments of each approach areprovided alongside an in-depth discussion that offers insight for futureresearch. To promote the advancement of this field, our benchmark, dataset, andcode are made publicly available.

3D-MIR: A Benchmark and Empirical Study on 3D Medical Image Retrieval in Radiology