Abstract
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) 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; and 2) Unlike typical scenarios where each representation is based on a single image, cell profiling often involves multiple input images, making it difficult to effectively combine all available information. To overcome these challenges, we propose SSLProfiler, a non-contrastive SSL framework specifically designed for cell profiling. 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, SSLProfiler won the Cell Line Transferability challenge at CVPR 2025.
Abstract (translated)
基于图像的细胞分析旨在创建具有信息量的细胞图像表示。这一技术在药物发现中至关重要，并且随着计算机视觉领域的近期进展得到了显著提升。受最近非对比自监督学习（SSL）发展的启发，本文初步探索了使用此类方法训练适用于细胞图像的一般化特征提取器的可能性。然而，存在两大挑战：1) 细胞图像与自然图像的分布差异很大，导致现有SSL方法中的视图生成过程失效；2) 与其他场景不同的是，在典型的场景中每个表示基于单一图像，而细胞分析通常涉及多张输入图像，这使得有效整合所有可用信息变得困难。为克服这些挑战，我们提出了一种专门针对细胞分析的非对比自监督学习框架——SSLProfiler。我们引入了专用于细胞图像的数据增强和表征后处理方法，有效地解决了上述问题，并生成了一个稳健的特征提取器。凭借这些改进，SSLProfiler在CVPR 2025的Cell Line Transferability挑战赛中胜出。
URL
https://arxiv.org/abs/2506.14265
