  Reliable large-scale cell detection and segmentation is the fundamental first
step to understanding biological processes in the brain. The ability to
phenotype cells at scale can accelerate preclinical drug evaluation and
system-level brain histology studies. The impressive advances in deep learning
offer a practical solution to cell image detection and segmentation.
Unfortunately, categorizing cells and delineating their boundaries for training
deep networks is an expensive process that requires skilled biologists. This
paper presents a novel self-supervised Dual-Loss Adaptive Masked Autoencoder
(DAMA) for learning rich features from multiplexed immunofluorescence brain
images. DAMA's objective function minimizes the conditional entropy in
pixel-level reconstruction and feature-level regression. Unlike existing
self-supervised learning methods based on a random image masking strategy, DAMA
employs a novel adaptive mask sampling strategy to maximize mutual information
and effectively learn brain cell data. To the best of our knowledge, this is
the first effort to develop a self-supervised learning method for multiplexed
immunofluorescence brain images. Our extensive experiments demonstrate that
DAMA features enable superior cell detection, segmentation, and classification
performance without requiring many annotations.

Computer Vision and Pattern Recognition