Abstract
Biomedical imaging and RNA sequencing with single-cell resolution improves our understanding of white blood cell diseases like leukemia. By combining morphological and transcriptomic data, we can gain insights into cellular functions and trajectories involved in blood cell differentiation. However, existing methodologies struggle with integrating morphological and transcriptomic data, leaving a significant research gap in comprehensively understanding the dynamics of cell dif ferentiation. Here, we introduce an unsupervised method that explores and reconstructs these two modalities and uncovers the relationship between different subtypes of white blood cells from human peripheral blood smears in terms of morphology and their corresponding transcriptome. Our method is based on a beta-variational autoencoder (β-VAE) with a customized loss function, incorporating a R-CNN architecture to distinguish single-cell from background and to minimize any interference from artifacts. This implementation of β-VAE shows good reconstruction capability along with continuous latent embeddings, while maintaining clear differentiation between single-cell classes. Our novel approach is especially helpful to uncover the correlation of two latent features in complex biological processes such as formation of granules in the cell (granulopoiesis) with gene expression patterns. It thus provides a unique tool to improve the understanding of white blood cell maturation for biomedicine and diagnostics.
Links to Paper and Supplementary Materials
Main Paper (Open Access Version): https://papers.miccai.org/miccai-2026-sat/paper/AIIG2024_007.pdf
SharedIt Link: Not yet available
SpringerLink (DOI): Not yet available
Supplementary Material: Not Submitted
Link to Open Review
Open Review Page: Not Available
BibTex
@InProceedings{MerGiz_Multimodal_MICCAISAT2026,
author = { Mert, Gizem AND Sadafi, Ario AND Salehi, Raheleh AND Navab, Nassir AND Marr, Carsten},
title = { { Multimodal Analysis of White Blood Cell Differentiation in Acute Myeloid Leukemia Patients using a β-Variational Autoencoder } },
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026 Workshops and Challenges},
year = {2026},
publisher = {Springer Nature Switzerland},
volume = {LNCS 17252},
month = {pending},
page = {pending}
}