Monet: Multi-Scale Overlap Network For Duplication Detection in Biomedical Images

Deep learning has proven to be a very efficient tool to help pathologists analyze Whole Slide Images (WSI) toward au- tomated classification or segmentation of detailed structures such as nuclei, glands or glomeruli, that are key for disease diagnosis and staging. Many deep learning methods have shown impressive performance but are still imperfect, while manual segmentation has poor inter-rater agreement. in this paper, we propose a patch-level automated correction of a given baseline initial segmentation, based on deep-learning of segmentation errors and downstream local refinments. Re- sults on the MoNuSeg and PanNuke test datasets show signif- icant improvement of nuclei segmentation quality.