Efficient Cell Painting Image Representation Learning
Автор: Sentient Systems
Загружено: 2025-11-15
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Описание: The provided text is an abstract and submission information for an academic paper titled "Efficient Cell Painting Image Representation Learning via Cross-Well Aligned Masked Siamese Network," submitted to arXiv under the category of Computer Vision and Pattern Recognition. This research introduces a novel framework, CWA-MSN, designed to create meaningful and batch-robust cellular representations from cell painting images, which is critical for accelerating drug discovery. The authors propose that CWA-MSN aligns cell embeddings across different wells, ensuring semantic consistency despite batch effects, while maintaining high data and parameter efficiency. Specifically, the paper claims that CWA-MSN significantly outperforms existing state-of-the-art methods like OpenPhenom and CellCLIP on a gene-gene relationship retrieval benchmark, often using substantially fewer data and parameters. Ultimately, the paper demonstrates an effective and simple method for learning cell image representations, even when budgets for data and parameters are limited.
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