RepMode: Learning to Re-parameterize Diverse Experts for Subcellular Structure Prediction

• URL: http://arxiv.org/abs/2212.10066v2
• Date: Sat, 25 Mar 2023 06:48:53 GMT
• Title: RepMode: Learning to Re-parameterize Diverse Experts for Subcellular Structure Prediction
• Authors: Donghao Zhou, Chunbin Gu, Junde Xu, Furui Liu, Qiong Wang, Guangyong Chen, Pheng-Ann Heng
• Abstract summary: In biological research, fluorescence staining is a key technique to reveal the locations and morphology of subcellular structures. 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. We propose RepMode, a network that dynamically organizes its parameters with task-aware priors to handle specified single-label prediction tasks.
• Score: 54.69195221765405
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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