Example image pair used in the CBIR experiments (BF on the left, SHG on the right). This image pair is shown aligned, without the rotations and translations used in the test set. In addition, the smaller image cutouts show patches used in the s-CBIR experiments, with the orange squares indicating how the patches were cropped.

Blog

October 1, 2024

Publication

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

August 13, 2024

Breznik, Eva; Wetzer, Elisabeth; Lindblad, Joakim; Sladoje, Nataša

Paper 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 and reliable retrieval. We illustrate its advantages through a replacement study, exploring a number of feature extractors and learned representations, as well as through comparison to recent (cross-modality) CBIR methods. For the task of (sub-)image retrieval on a (publicly available) dataset of brightfield and second harmonic generation microscopy images, the results show that our approach is superior to all tested alternatives. We discuss the shortcomings of the compared methods and observe the importance of equivariance and invariance properties of the learned representations and feature extractors in the CBIR pipeline.

Code is available at: https://github.com/MIDA-group/CrossModal_ImgRetrieval.