{"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/adaptive-feature-recombination-and","title":"Adaptive feature recombination and recalibration for semantic segmentation: application to brain tumor segmentation in MRI","arxiv_id":"1806.02318","date":"2018-06-06","proceeding":null,"authors":["Sérgio Pereira","Victor Alves","Carlos A. Silva"],"abstract":"Convolutional neural networks (CNNs) have been successfully used for brain\ntumor segmentation, specifically, fully convolutional networks (FCNs). FCNs can\nsegment a set of voxels at once, having a direct spatial correspondence between\nunits in feature maps (FMs) at a given location and the corresponding\nclassified voxels. In convolutional layers, FMs are merged to create new FMs,\nso, channel combination is crucial. However, not all FMs have the same\nrelevance for a given class. Recently, in classification problems,\nSqueeze-and-Excitation (SE) blocks have been proposed to re-calibrate FMs as a\nwhole, and suppress the less informative ones. However, this is not optimal in\nFCN due to the spatial correspondence between units and voxels. In this\narticle, we propose feature recombination through linear expansion and\ncompression to create more complex features for semantic segmentation.\nAdditionally, we propose a segmentation SE (SegSE) block for feature\nrecalibration that collects contextual information, while maintaining the\nspatial meaning. Finally, we evaluate the proposed methods in brain tumor\nsegmentation, using publicly available data.","url_abs":"http://arxiv.org/abs/1806.02318v1","url_pdf":"http://arxiv.org/pdf/1806.02318v1.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":"adaptive-feature-recombination-and","repo_url":"https://github.com/sergiormpereira/rr_segse","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"brain-tumor-segmentation","task_name":"Brain Tumor Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"tumor-segmentation","task_name":"Tumor 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}