{"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/multi-level-contextual-network-for-biomedical","title":"Multi-Level Contextual Network for Biomedical Image Segmentation","arxiv_id":"1810.00327","date":"2018-09-30","proceeding":null,"authors":["Amirhossein Dadashzadeh","Alireza Tavakoli Targhi"],"abstract":"Accurate and reliable image segmentation is an essential part of biomedical\nimage analysis. In this paper, we consider the problem of biomedical image\nsegmentation using deep convolutional neural networks. We propose a new\nend-to-end network architecture that effectively integrates local and global\ncontextual patterns of histologic primitives to obtain a more reliable\nsegmentation result. Specifically, we introduce a deep fully convolution\nresidual network with a new skip connection strategy to control the contextual\ninformation passed forward. Moreover, our trained model is also computationally\ninexpensive due to its small number of network parameters. We evaluate our\nmethod on two public datasets for epithelium segmentation and tubule\nsegmentation tasks. Our experimental results show that the proposed method\nprovides a fast and effective way of producing a pixel-wise dense prediction of\nbiomedical images.","url_abs":"http://arxiv.org/abs/1810.00327v1","url_pdf":"http://arxiv.org/pdf/1810.00327v1.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":"multi-level-contextual-network-for-biomedical","repo_url":"https://github.com/Plrbear/biomedical-image-segmentation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"image-segmentation","task_name":"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}