{"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/deep-adversarial-training-for-multi-organ","title":"Deep Adversarial Training for Multi-Organ Nuclei Segmentation in Histopathology Images","arxiv_id":"1810.00236","date":"2018-09-29","proceeding":null,"authors":["Faisal Mahmood","Daniel Borders","Richard Chen","Gregory N. McKay","Kevan J. Salimian","Alexander Baras","Nicholas J. Durr"],"abstract":"Nuclei segmentation is a fundamental task that is critical for various\ncomputational pathology applications including nuclei morphology analysis, cell\ntype classification, and cancer grading. Conventional vision-based methods for\nnuclei segmentation struggle in challenging cases and deep learning approaches\nhave proven to be more robust and generalizable. However, CNNs require large\namounts of labeled histopathology data. Moreover, conventional CNN-based\napproaches lack structured prediction capabilities which are required to\ndistinguish overlapping and clumped nuclei. Here, we present an approach to\nnuclei segmentation that overcomes these challenges by utilizing a conditional\ngenerative adversarial network (cGAN) trained with synthetic and real data. We\ngenerate a large dataset of H&E training images with perfect nuclei\nsegmentation labels using an unpaired GAN framework. This synthetic data along\nwith real histopathology data from six different organs are used to train a\nconditional GAN with spectral normalization and gradient penalty for nuclei\nsegmentation. This adversarial regression framework enforces higher order\nconsistency when compared to conventional CNN models. We demonstrate that this\nnuclei segmentation approach generalizes across different organs, sites,\npatients and disease states, and outperforms conventional approaches,\nespecially in isolating individual and overlapping nuclei.","url_abs":"http://arxiv.org/abs/1810.00236v2","url_pdf":"http://arxiv.org/pdf/1810.00236v2.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":"deep-adversarial-training-for-multi-organ","repo_url":"https://github.com/faisalml/NucleiSegmentation","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":null,"task_name":"Generative Adversarial Network"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"structured-prediction","task_name":"Structured Prediction"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"spectral-normalization","method_name":"Spectral Normalization"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1810.00236","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}