Abstract
Cell detection is an important topic in biomedical image analysis and it is often the prerequisite for the following segmentation or classification procedures. In this chapter, we propose a novel algorithm for general cell detection problem: First, a set of cell detection candidates is generated using different algorithms with varying parameters. Second, each candidate is assigned a score by a trained deep convolutional neural network (DCNN). Finally, a subset of best detection results are selected from all candidates to compose the final cell detection results. The subset selection task is formalized as a maximum-weight independent set problem, which is designed to find the heaviest subset of mutually nonadjacent nodes in a graph. Experiments show that the proposed general cell detection algorithm provides detection results that are dramatically better than any individual cell detection algorithm.
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Liu, F., Yang, L. (2017). A Novel Cell Detection Method Using Deep Convolutional Neural Network and Maximum-Weight Independent Set. In: Lu, L., Zheng, Y., Carneiro, G., Yang, L. (eds) Deep Learning and Convolutional Neural Networks for Medical Image Computing. Advances in Computer Vision and Pattern Recognition. Springer, Cham. https://doi.org/10.1007/978-3-319-42999-1_5
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DOI: https://doi.org/10.1007/978-3-319-42999-1_5
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