OrgaCast: A Trustworthy Spatiotemporal Diffusion Model for Fluorescence Organoid Forecasting
Dawei Gao, Angello Huerta Gomez, Mingchen Li, Marcel El-Mokahal, Huaxiao Yang, Yunhe Feng
Abstract
Accurately forecasting the spatiotemporal dynamics of biological systems, such as human pluripotent stem cell (hPSC)-derived cardiac organoids, from microscopy time-series is a critical challenge in biomedicine with profound implications for drug discovery. Existing generative models often fail to capture the intricate dynamics of organoid development, struggling with their irregular morphology, indistinct boundaries, and complex spatiotemporal patterns. To overcome these limitations, we introduce OrgaCast, a novel multimodal conditional diffusion model for high-fidelity organoid forecasting. OrgaCast uniquely conditions the generative process on three synergistic modalities: (i) historical image sequences, captured by a dedicated spatiotemporal control module; (ii) structured numerical metadata defining experimental conditions; and (iii) descriptive text captions summarizing the biological context. This comprehensive conditioning enables the generation of forecasts with high visual accuracy and biological plausibility. Furthermore, to enhance the model
BibTeX
@inproceedings{aaai2026_orgacastatrustwo,
title = {OrgaCast: A Trustworthy Spatiotemporal Diffusion Model for Fluorescence Organoid Forecasting},
author = {Dawei Gao and Angello Huerta Gomez and Mingchen Li and Marcel El-Mokahal and Huaxiao Yang and Yunhe Feng},
booktitle = {AAAI 2026},
year = {2026}
}