Inferring biological relationships from cellular phenotypes in high-content
microscopy screens provides significant opportunity and challenge in biological
research. Prior results have shown that deep vision models can capture
biological signal better than hand-crafted features. This work explores how
weakly supervised and self-supervised deep learning approaches scale when
training larger models on larger datasets. Our results show that both CNN- and
ViT-based masked autoencoders significantly outperform weakly supervised
models. At the high-end of our scale, a ViT-L/8 trained on over 3.5-billion
unique crops sampled from 95-million microscopy images achieves relative
improvements as high as 28% over our best weakly supervised models at inferring
known biological relationships curated from public databases.

通过高内容显微镜筛选从细胞表型中推断生物关系在生物研究中提供了重要的机遇和挑战。该研究探讨了在更大数据集上训练更大模型时，弱监督和自监督深度学习方法的可扩展性。结果显示，基于 CNN 和 ViT 的掩蔽自编码器在表型推断方面明显优于弱监督模型。在我们的尺度上限，通过来自 9500 万显微镜图像的 35 亿个独特裁剪样本训练的 ViT-L/8 相对于从公共数据库中整理的已知生物关系的最佳弱监督模型的相对改善率达到了 28%。