Nuclei detection in breast histopathology images with iterative correction

This work presents a deep network architecture to improve nuclei detection performance and achieve the high localization accuracy of nuclei in breast cancer histopathology images. The proposed model consists of two parts, generating nuclear candidate module and refining nuclear localization module. We first design a novel patch learning method to obtain high-quality nuclear candidates, where in addition to categories, location representations are also added to the patch information to implement the multi-task learning process of nuclear classification and localization; meanwhile, the deep supervision mechanism is introduced to obtain the coherent contributions from each scale layer. In order to refine nuclear localization, we propose an iterative correction strategy to make the prediction progressively closer to the ground truth, which significantly improves the accuracy of nuclear localization and facilitates neighbor size selection in the nonmaximum suppression step. Experimental results demonstrate the superior performance of our method for nuclei detection on the H&E stained histopathological image dataset as compared to previous state-of-the-art methods, especially in multiple cluttered nuclei detection, can achieve better results than existing techniques.