Video Diffusion models for the apoptosis forcasting

Abstract

Reliable and early prediction of cell death (apoptosis) is critically important in various areas of biology, particularly in characterizing the effectiveness of cell-based infusion products utilized for cancer immunotherapy. While deep Convolutional Neural Networks (CNNs) are often used in state-of-the-art approaches for apoptosis classification, they typically focus solely on individual cells and ignore cell-cell interaction. To address this limitation, we propose a novel generative approach based on a video diffusion model, which predicts future cellular behaviors for early detection of apoptosis events, even before molecular markers like Annexin-V or visual indications like membrane blebbing become apparent. Our approach accounts for the interactions of multiple target cells and their spatial and temporal relationships at each time frame. We condition our generative model on two starting frames and utilize an auto-regressive framework to predict the subsequent five frames. Our model achieves a 0.88 F1 score on the cell death event classification and 2.11 mean absolute death-time error, significantly outperforming state-of-the-art methods.