针对细胞图像跨站点一致性，提出局部聚合自监督学习框架。
Self-supervised Representation Learning with Local Aggregation for Image-based Profiling
- 设计局部聚合机制融合多张细胞图像信息。
- 通过定制化增强与后处理，提升跨站点特征一致性。
- 在CVPR 2025细胞系迁移挑战赛中胜出，适合药物发现场景。
基于图像的细胞表型分析旨在生成细胞图像的丰富表征，对药物发现至关重要，并随计算机视觉进步显著发展。受非对比性自监督学习（SSL）启发，本文首次探索使用此类方法训练通用细胞图像特征提取器。然而存在两大挑战：其一，细胞表型常需多张输入图像，难以有效融合全部信息；其二，细胞图像与自然图像分布差异大，导致现有SSL方法中的视图生成策略失效。为此，我们提出具有局部聚合的自监督框架，以增强细胞表征的跨站点一致性。引入专为细胞图像设计的数据增强和表征后处理方法，有效解决上述问题，构建出鲁棒特征提取器。经验证，该框架在CVPR 2025细胞系迁移性挑战赛中夺冠。
原文摘要 · Abstract (English)
Image-based cell profiling aims to create informative representations of cell images. This technique is critical in drug discovery and has greatly advanced with recent improvements in computer vision. Inspired by recent developments in non-contrastive Self-Supervised Learning (SSL), this paper provides an initial exploration into training a generalizable feature extractor for cell images using such methods. However, there are two major challenges: 1) Unlike typical scenarios where each representation is based on a single image, cell profiling often involves multiple input images, making it difficult to effectively fuse all available information; and 2) There is a large difference between the distributions of cell images and natural images, causing the view-generation process in existing SSL methods to fail. To address these issues, we propose a self-supervised framework with local aggregation to improve cross-site consistency of cell representations. We introduce specialized data augmentation and representation post-processing methods tailored to cell images, which effectively address the issues mentioned above and result in a robust feature extractor. With these improvements, the proposed framework won the Cell Line Transferability challenge at CVPR 2025.
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