Application of Self Supervised Vision Transformers for Multiplexed Microscopy Images and Its Challenges

Multiplexed microscopy imaging enables the simultaneous use of numerous fluorescent markers on one biological sample. This technique is especially useful in cancer research, cellular and molecular biology, and drug discovery. Studying these microscopic images is challenging due to the large scale of datasets, number of channels that exceeds the natural imaging domain, and the lack of annotations. In this work, we applied a self-supervised learning method for representation learning, and then studied the quality of the learned representations visually and by classification tasks. Results show that although the model creates similar feature embeddings for the same metadata labels, the model also captures some technical variation between slides.