Abstract
Recent advances in deep learning have achieved impressive results on microscopy cell counting tasks. The success of deep learning models usually needs sufficient training data with manual annotations, which can be time-consuming and costly. In this paper, we propose an annotation-efficient cell counting approach which injects cell counting networks into an active learning framework. By designing a multi-task learning in the cell counter network model, we leverage unlabeled data for feature representation learning and use deep clustering to group unlabeled data. Rather than labeling every cell in each training image, the deep active learning only suggests the most uncertain, diverse, representative and rare image regions for annotation. Evaluated on four widely used cell counting datasets, our cell counter trained by a small subset of training data suggested by the deep active learning, achieves superior performance compared to state-of-the-arts with full training or other suggestive annotations. Our code is available at https://github.com/cvbmi-research/AnnotationEfficient-CellCounting.
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Acknowledgement
This project was supported by Stony Brook University - Brookhaven National Laboratory (SBU-BNL) seed grant on annotation-efficient deep learning.
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Wang, Z., Yin, Z. (2021). Annotation-Efficient Cell Counting. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12908. Springer, Cham. https://doi.org/10.1007/978-3-030-87237-3_39
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