{"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/cancer-metastasis-detection-with-neural","title":"Cancer Metastasis Detection With Neural Conditional Random Field","arxiv_id":"1806.07064","date":"2018-06-19","proceeding":null,"authors":["Yi Li","Wei Ping"],"abstract":"Breast cancer diagnosis often requires accurate detection of metastasis in\nlymph nodes through Whole-slide Images (WSIs). Recent advances in deep\nconvolutional neural networks (CNNs) have shown significant successes in\nmedical image analysis and particularly in computational histopathology.\nBecause of the outrageous large size of WSIs, most of the methods divide one\nslide into lots of small image patches and perform classification on each patch\nindependently. However, neighboring patches often share spatial correlations,\nand ignoring these spatial correlations may result in inconsistent predictions.\nIn this paper, we propose a neural conditional random field (NCRF) deep\nlearning framework to detect cancer metastasis in WSIs. NCRF considers the\nspatial correlations between neighboring patches through a fully connected CRF\nwhich is directly incorporated on top of a CNN feature extractor. The whole\ndeep network can be trained end-to-end with standard back-propagation algorithm\nwith minor computational overhead from the CRF component. The CNN feature\nextractor can also benefit from considering spatial correlations via the CRF\ncomponent. Compared to the baseline method without considering spatial\ncorrelations, we show that the proposed NCRF framework obtains probability maps\nof patch predictions with better visual quality. We also demonstrate that our\nmethod outperforms the baseline in cancer metastasis detection on the\nCamelyon16 dataset and achieves an average FROC score of 0.8096 on the test\nset. NCRF is open sourced at https://github.com/baidu-research/NCRF.","url_abs":"http://arxiv.org/abs/1806.07064v1","url_pdf":"http://arxiv.org/pdf/1806.07064v1.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":"cancer-metastasis-detection-with-neural","repo_url":"https://github.com/baidu-research/NCRF","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"cancer-metastasis-detection","task_name":"Cancer Metastasis Detection"},{"task_slug":"medical-image-analysis","task_name":"Medical Image Analysis"},{"task_slug":"whole-slide-images","task_name":"whole slide images"}],"methods":[{"method_slug":"crf","method_name":"CRF"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1806.07064","atlas_url":"https://app.syntology.ai/?focus=1806.07064","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}