{"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/xnet-a-convolutional-neural-network-cnn","title":"XNet: A convolutional neural network (CNN) implementation for medical X-Ray image segmentation suitable for small datasets","arxiv_id":"1812.00548","date":"2018-12-03","proceeding":null,"authors":["Joseph Bullock","Carolina Cuesta-Lazaro","Arnau Quera-Bofarull"],"abstract":"X-Ray image enhancement, along with many other medical image processing\napplications, requires the segmentation of images into bone, soft tissue, and\nopen beam regions. We apply a machine learning approach to this problem,\npresenting an end-to-end solution which results in robust and efficient\ninference. Since medical institutions frequently do not have the resources to\nprocess and label the large quantity of X-Ray images usually needed for neural\nnetwork training, we design an end-to-end solution for small datasets, while\nachieving state-of-the-art results. Our implementation produces an overall\naccuracy of 92%, F1 score of 0.92, and an AUC of 0.98, surpassing classical\nimage processing techniques, such as clustering and entropy based methods,\nwhile improving upon the output of existing neural networks used for\nsegmentation in non-medical contexts. The code used for this project is\navailable online.","url_abs":"http://arxiv.org/abs/1812.00548v2","url_pdf":"http://arxiv.org/pdf/1812.00548v2.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":"xnet-a-convolutional-neural-network-cnn","repo_url":"https://github.com/JosephPB/XNet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"xnet-a-convolutional-neural-network-cnn","repo_url":"https://github.com/mmfrxx/X-net","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"image-enhancement","task_name":"Image Enhancement"},{"task_slug":"image-segmentation","task_name":"Image Segmentation"},{"task_slug":"medical-x-ray-image-segmentation","task_name":"Medical X-Ray Image Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}