Cross-modality sub-image retrieval using contrastive multimodal image representations.

ABSTRACT: In tissue characterization and cancer diagnostics, multimodal imaging has emerged as a powerful technique. Thanks to computational advances, large datasets can be exploited to discover patterns in pathologies and improve diagnosis. However, this requires efficient and scalable image retrieval methods. Cross-modality image retrieval is particularly challenging, since images of similar (or even the same) content captured by different modalities might share few common structures. We propose a new application-independent content-based image retrieval (CBIR) system for reverse (sub-)image search across modalities, which combines deep learning to generate representations (embedding the different modalities in a common space) with robust feature extraction and bag-of-words models for efficient an

SUBMITTER: Breznik E 

PROVIDER: S-EPMC11322435 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature