{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/mask-rcnn-and-u-net-ensembled-for-nuclei","title":"Mask-RCNN and U-net Ensembled for Nuclei Segmentation","arxiv_id":"1901.10170","date":"2019-01-29","proceeding":null,"authors":["Aarno Oskar Vuola","Saad Ullah Akram","Juho Kannala"],"abstract":"Nuclei segmentation is both an important and in some ways ideal task for\nmodern computer vision methods, e.g. convolutional neural networks. While\nrecent developments in theory and open-source software have made these tools\neasier to implement, expert knowledge is still required to choose the right\nmodel architecture and training setup. We compare two popular segmentation\nframeworks, U-Net and Mask-RCNN in the nuclei segmentation task and find that\nthey have different strengths and failures. To get the best of both worlds, we\ndevelop an ensemble model to combine their predictions that can outperform both\nmodels by a significant margin and should be considered when aiming for best\nnuclei segmentation performance.","url_abs":"http://arxiv.org/abs/1901.10170v1","url_pdf":"http://arxiv.org/pdf/1901.10170v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"mask-rcnn-and-u-net-ensembled-for-nuclei","repo_url":"https://github.com/AbdulrahmanCE/-pneumonia-detection-","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"segmentation","task_name":"Segmentation"}],"methods":[{"method_slug":"concatenated-skip-connection","method_name":"Concatenated Skip Connection"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"u-net","method_name":"U-Net"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}