Efficient Cell Painting Image Representation Learning via Cross-Well Aligned Masked Siamese Network

• URL: http://arxiv.org/abs/2509.19896v1
• Date: Wed, 24 Sep 2025 08:48:29 GMT
• Title: Efficient Cell Painting Image Representation Learning via Cross-Well Aligned Masked Siamese Network
• Authors: Pin-Jui Huang, Yu-Hsuan Liao, SooHeon Kim, NoSeong Park, JongBae Park, DongMyung Shin,
• Abstract summary: We present Cross-Well Aligned Masked Siamese Network (CWA-MSN), a novel representation learning framework.<n>CWA-MSN aligns embeddings of cells subjected to the same perturbation across different wells, enforcing semantic consistency despite batch effects.<n>In gene-gene relationship retrieval benchmark, CWA-MSN outperforms state-of-the-art publicly available self-supervised (OpenPhenom) and contrastive learning (CellCLIP) methods.
• Score: 21.126506900168398
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Computational models that predict cellular phenotypic responses to chemical and genetic perturbations can accelerate drug discovery by prioritizing therapeutic hypotheses and reducing costly wet-lab iteration. However, extracting biologically meaningful and batch-robust cell painting representations remains challenging. Conventional self-supervised and contrastive learning approaches often require a large-scale model and/or a huge amount of carefully curated data, still struggling with batch effects. We present Cross-Well Aligned Masked Siamese Network (CWA-MSN), a novel representation learning framework that aligns embeddings of cells subjected to the same perturbation across different wells, enforcing semantic consistency despite batch effects. Integrated into a masked siamese architecture, this alignment yields features that capture fine-grained morphology while remaining data- and parameter-efficient. For instance, in a gene-gene relationship retrieval benchmark, CWA-MSN outperforms the state-of-the-art publicly available self-supervised (OpenPhenom) and contrastive learning (CellCLIP) methods, improving the benchmark scores by +29\% and +9\%, respectively, while training on substantially fewer data (e.g., 0.2M images for CWA-MSN vs. 2.2M images for OpenPhenom) or smaller model size (e.g., 22M parameters for CWA-MSN vs. 1.48B parameters for CellCLIP). Extensive experiments demonstrate that CWA-MSN is a simple and effective way to learn cell image representation, enabling efficient phenotype modeling even under limited data and parameter budgets.

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