Trying a Channel-Agnostic Microscopy Foundation Model
This project was part of a small group benchmark on foundation models for multiplexed fluorescence microscopy. The general question was simple: if we take recent pretrained models and run them on real multiplexed microscopy data, what do their embeddings actually capture? My part focused on a channel-agnostic masked autoencoder, or CA-MAE, from the paper Masked Autoencoders for Microscopy are Scalable Learners of Cellular Biology. I did not train a new foundation model here, and I did not fine-tune the model. The work was more practical than that: take a pretrained model, make our data fit the input format, run inference on the university cluster, and then look at the resulting embeddings carefully. ...