Abstract: Cell nuclei detection is the basis for many tasks in Computational Pathology ranging from cancer diagnosis to survival analysis. It is a challenging task due to the significant inter/intra-class variation of cellular morphology. The problem is aggravated by the need for additional accurate localization of the nuclei for downstream applications. Most of the existing methods regress the probability of each pixel being a nuclei centroid, while relying on post-processing to implicitly infer the rough location of nuclei centers. To solve this problem we propose a novel multi-task learning framework called vector oriented confidence accumulation (VOCA) based on deep convolutional encoder-decoder. The model learns a confidence score, localization vector and weight of contribution for each pixel. The three tasks are trained concurrently and the confidence of pixels are accumulated according to the localization vectors in detection stage to generate a sparse map that describes accurate and precise cell locations. A detailed comparison to the state-of-the-art based on a publicly available colorectal cancer dataset showed superior detection performance and significantly higher localization accuracy.
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