Exploring Non-contrastive Self-supervised Representation Learning for Image-based Profiling
Journal:
arXiv
Published Date:
Jun 17, 2025
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.
