Abstract
Nuclei detection is an important task in the histology domain as it is a main step toward further analysis such as cell counting, cell segmentation, study of cell connections, etc. This is a challenging task due to complex texture of histology image, variation in shape, and touching cells. To tackle these hurdles, many approaches have been proposed in the literature where deep learning methods stand on top in terms of performance. Hence, in this paper, we propose a novel framework for nuclei detection based on Mixture Density Networks (MDNs). These networks are suitable to map a single input to several possible outputs and we utilize this property to detect multiple seeds in a single image patch. A new modified form of a cost function is proposed for training and handling patches with missing nuclei. The probability maps of the nuclei in the individual patches are next combined to generate the final image-wide result. The experimental results show the state-of-the-art performance on complex colorectal adenocarcinoma dataset.
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Koohababni, N.A., Jahanifar, M., Gooya, A., Rajpoot, N. (2018). Nuclei Detection Using Mixture Density Networks. In: Shi, Y., Suk, HI., Liu, M. (eds) Machine Learning in Medical Imaging. MLMI 2018. Lecture Notes in Computer Science(), vol 11046. Springer, Cham. https://doi.org/10.1007/978-3-030-00919-9_28
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DOI: https://doi.org/10.1007/978-3-030-00919-9_28
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