  In biological research, fluorescence staining is a key technique to reveal
the locations and morphology of subcellular structures. However, it is slow,
expensive, and harmful to cells. In this paper, we model it as a deep learning
task termed subcellular structure prediction (SSP), aiming to predict the 3D
fluorescent images of multiple subcellular structures from a 3D
transmitted-light image. Unfortunately, due to the limitations of current
biotechnology, each image is partially labeled in SSP. Besides, naturally,
subcellular structures vary considerably in size, which causes the multi-scale
issue of SSP. To overcome these challenges, we propose Re-parameterizing
Mixture-of-Diverse-Experts (RepMode), a network that dynamically organizes its
parameters with task-aware priors to handle specified single-label prediction
tasks. In RepMode, the Mixture-of-Diverse-Experts (MoDE) block is designed to
learn the generalized parameters for all tasks, and gating re-parameterization
(GatRep) is performed to generate the specialized parameters for each task, by
which RepMode can maintain a compact practical topology exactly like a plain
network, and meanwhile achieves a powerful theoretical topology. Comprehensive
experiments show that RepMode can achieve state-of-the-art overall performance
in SSP.
