Abstract
The morphology of pyramidal cells (PCs) varies significantly among species and brain layers. Therefore, it is particularly challenging to analyze which species or layers they belong to based on morphological features. Existing deep learning-based methods analyze species-related or layer-related morphological characteristics of PCs. However, these methods are realized in a task-agnostic manner without considering task-specific features. This paper proposes a task-specific morphological representation learning framework for morphology analysis of PCs to enforce task-specific feature extraction through dual-task learning, enabling performance gains for each task. Specifically, we first utilize species-wise and layer-wise feature extraction branches to obtain species-related and layer-related features. Applying the principle of mutual information minimization, we then explicitly force each branch to learn task-specific features, which are further enhanced via an adaptive representation enhancement module. In this way, the performance of both tasks can be greatly improved simultaneously. Experimental results demonstrate that the proposed method can effectively extract the species-specific and layer-specific representations when identifying rat and mouse PCs in multiple brain layers. Our method reaches the accuracies of 87.44% and 72.46% on species and layer analysis tasks, significantly outperforming a single task by 2.22% and 3.86%, respectively.
C. Sun and Q. Guo—Equal contributions.
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Notes
- 1.
Note that task-related features contain task-specific and common features.
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Acknowledgments
This work was supported by the JKW Research Funds (20-163-14-LZ-001-004-01) and the Anhui Provincial Natural Science Foundation (2108085UD12). We acknowledge the support of GPU cluster built by MCC Lab of Information Science and Technology Institution, USTC.
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Sun, C., Guo, Q., Yang, G., Zhao, F. (2023). Learning Task-Specific Morphological Representation for Pyramidal Cells via Mutual Information Minimization. In: Rekik, I., Adeli, E., Park, S.H., Cintas, C., Zamzmi, G. (eds) Predictive Intelligence in Medicine. PRIME 2023. Lecture Notes in Computer Science, vol 14277. Springer, Cham. https://doi.org/10.1007/978-3-031-46005-0_12
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