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

In tissue characterization and cancer diagnostics, multimodal imaging hasemerged as a powerful technique. Thanks to computational advances, largedatasets can be exploited to discover patterns in pathologies and improvediagnosis. However, this requires efficient and scalable image retrievalmethods. Cross-modality image retrieval is particularly challenging, sinceimages of similar (or even the same) content captured by different modalitiesmight share few common structures. We propose a new application-independentcontent-based image retrieval (CBIR) system for reverse (sub-)image searchacross modalities, which combines deep learning to generate representations(embedding the different modalities in a common space) with classical featureextraction and bag-of-words models for efficient and reliable retrieval. Weillustrate its advantages through a replacement study, exploring a number offeature extractors and learned representations, as well as through comparisonto recent (cross-modality) CBIR methods. For the task of (sub-)image retrievalon a (publicly available) dataset of brightfield and second harmonic generationmicroscopy images, the results show that our approach is superior to all testedalternatives. We discuss the shortcomings of the compared methods and observethe importance of equivariance and invariance properties of the learnedrepresentations and feature extractors in the CBIR pipeline. Code is availableat: \url{https://github.com/MIDA-group/CrossModal_ImgRetrieval}.

Cross-Modality Sub-Image Retrieval using Contrastive Multimodal Image Representations