Out-of-distribution evaluations of channel agnostic masked autoencoders in fluorescence microscopy
Journal:
arXiv
Published Date:
Mar 24, 2025
Abstract
Developing computer vision for high-content screening is challenging due to
various sources of distribution-shift caused by changes in experimental
conditions, perturbagens, and fluorescent markers. The impact of different
sources of distribution-shift are confounded in typical evaluations of models
based on transfer learning, which limits interpretations of how changes to
model design and training affect generalisation. We propose an evaluation
scheme that isolates sources of distribution-shift using the JUMP-CP dataset,
allowing researchers to evaluate generalisation with respect to specific
sources of distribution-shift. We then present a channel-agnostic masked
autoencoder $\mathbf{Campfire}$ which, via a shared decoder for all channels,
scales effectively to datasets containing many different fluorescent markers,
and show that it generalises to out-of-distribution experimental batches,
perturbagens, and fluorescent markers, and also demonstrates successful
transfer learning from one cell type to another.
