{"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/dcsau-net-a-deeper-and-more-compact-split","title":"DCSAU-Net: A Deeper and More Compact Split-Attention U-Net for Medical Image Segmentation","arxiv_id":"2202.00972","date":"2022-02-02","proceeding":null,"authors":["Qing Xu","Zhicheng Ma","Na He","Wenting Duan"],"abstract":"Deep learning architecture with convolutional neural network (CNN) achieves outstanding success in the field of computer vision. Where U-Net, an encoder-decoder architecture structured by CNN, makes a great breakthrough in biomedical image segmentation and has been applied in a wide range of practical scenarios. However, the equal design of every downsampling layer in the encoder part and simply stacked convolutions do not allow U-Net to extract sufficient information of features from different depths. The increasing complexity of medical images brings new challenges to the existing methods. In this paper, we propose a deeper and more compact split-attention u-shape network (DCSAU-Net), which efficiently utilises low-level and high-level semantic information based on two novel frameworks: primary feature conservation and compact split-attention block. We evaluate the proposed model on CVC-ClinicDB, 2018 Data Science Bowl, ISIC-2018 and SegPC-2021 datasets. As a result, DCSAU-Net displays better performance than other state-of-the-art (SOTA) methods in terms of the mean Intersection over Union (mIoU) and F1-socre. More significantly, the proposed model demonstrates excellent segmentation performance on challenging images. The code for our work and more technical details can be found at https://github.com/xq141839/DCSAU-Net.","url_abs":"https://arxiv.org/abs/2202.00972v2","url_pdf":"https://arxiv.org/pdf/2202.00972v2.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":"dcsau-net-a-deeper-and-more-compact-split","repo_url":"https://github.com/xq141839/DCSAU-Net","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"image-segmentation","task_name":"Image Segmentation"},{"task_slug":"lesion-segmentation","task_name":"Lesion Segmentation"},{"task_slug":"medical-image-segmentation","task_name":"Medical Image Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[{"method_slug":"concatenated-skip-connection","method_name":"Concatenated Skip Connection"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"u-net","method_name":"U-Net"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/lesion-segmentation-on-isic-2018-task-1","task":"Lesion Segmentation","dataset":"ISIC 2018 Task 1","model":"DCSAU-Net","rank_in_archive_order":1,"of":1,"metrics":{"mIoU":"0.8301"},"uses_additional_data":false},{"leaderboard":"/sota/medical-image-segmentation-on-2018-data","task":"Medical Image Segmentation","dataset":"2018 Data Science Bowl","model":"DCSAU-Net","rank_in_archive_order":10,"of":10,"metrics":{"Recall":"0.9240","mIoU":"0.8501"},"uses_additional_data":false},{"leaderboard":"/sota/medical-image-segmentation-on-isic-2018","task":"Medical Image Segmentation","dataset":"ISIC 2018","model":"DCSAU-Net","rank_in_archive_order":2,"of":2,"metrics":{"DSC":"90.35"},"uses_additional_data":false},{"leaderboard":"/sota/medical-image-segmentation-on-isic2018","task":"Medical Image Segmentation","dataset":"ISIC2018","model":"U2netme","rank_in_archive_order":2,"of":3,"metrics":{"Accuracy":"0.94216","Precision":"0.89502","Test F1-Score":"0.90604","mean Dice":"0.905"},"uses_additional_data":false},{"leaderboard":"/sota/medical-image-segmentation-on-segpc-2021","task":"Medical Image Segmentation","dataset":"SegPC-2021","model":"DCSAU-Net","rank_in_archive_order":1,"of":1,"metrics":{"mIoU":"0.8048"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2202.00972","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}