Swiss Conference on Data Science 2022, Lucerne, Switzerland
June 23, 2022
In drug development, image-based bioassays are typically run-in high throughput on automated microscopes. The resulting cell imaging data come from multiple instruments and have been acquired at different time points, leading to technical and biological variation in the data, potentially hampering the quantitative analysis across an assay campaign. In this work, we analyze the robustness of a novel concept called Vision Transformers with respect to technical and biological variations. We compare their performance to recent analysis concepts by benchmarking the Cells Out of Sample dataset (COOS) from a high-content imaging screen. The experiments suggest that Vision Transformers are capable of learning more robust
representations, thereby even outperforming specially designed deep learning architectures by a large margin.