  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 the 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.
