{"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-scale-guided-attention-for-medical","title":"Multi-scale self-guided attention for medical image segmentation","arxiv_id":"1906.02849","date":"2019-06-07","proceeding":"arXiv preprint 2019 6","authors":["Ashish Sinha","Jose Dolz"],"abstract":"Even though convolutional neural networks (CNNs) are driving progress in medical image segmentation, standard models still have some drawbacks. First, the use of multi-scale approaches, i.e., encoder-decoder architectures, leads to a redundant use of information, where similar low-level features are extracted multiple times at multiple scales. Second, long-range feature dependencies are not efficiently modeled, resulting in non-optimal discriminative feature representations associated with each semantic class. In this paper we attempt to overcome these limitations with the proposed architecture, by capturing richer contextual dependencies based on the use of guided self-attention mechanisms. This approach is able to integrate local features with their corresponding global dependencies, as well as highlight interdependent channel maps in an adaptive manner. Further, the additional loss between different modules guides the attention mechanisms to neglect irrelevant information and focus on more discriminant regions of the image by emphasizing relevant feature associations. We evaluate the proposed model in the context of semantic segmentation on three different datasets: abdominal organs, cardiovascular structures and brain tumors. A series of ablation experiments support the importance of these attention modules in the proposed architecture. In addition, compared to other state-of-the-art segmentation networks our model yields better segmentation performance, increasing the accuracy of the predictions while reducing the standard deviation. This demonstrates the efficiency of our approach to generate precise and reliable automatic segmentations of medical images. Our code is made publicly available at https://github.com/sinAshish/Multi-Scale-Attention","url_abs":"https://arxiv.org/abs/1906.02849v3","url_pdf":"https://arxiv.org/pdf/1906.02849v3.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-scale-guided-attention-for-medical","repo_url":"https://github.com/sinAshish/Multi-Scale-Attention","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"attentive-segmentation-networks","task_name":"Attentive segmentation networks"},{"task_slug":"brain-tumor-segmentation","task_name":"Brain Tumor Segmentation"},{"task_slug":"deep-attention","task_name":"Deep Attention"},{"task_slug":"image-segmentation","task_name":"Image Segmentation"},{"task_slug":"medical-image-segmentation","task_name":"Medical Image Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/brain-tumor-segmentation-on-brats-2018","task":"Brain Tumor Segmentation","dataset":"BRATS 2018","model":"MS-Dual-Guided","rank_in_archive_order":2,"of":4,"metrics":{"Dice Score":"0.8037","MSD":"0.9","VS":"93.08"},"uses_additional_data":false},{"leaderboard":"/sota/medical-image-segmentation-on-chaos-mri","task":"Medical Image Segmentation","dataset":"CHAOS MRI Dataset","model":"MS-Dual-Guided","rank_in_archive_order":1,"of":1,"metrics":{"Dice Score":"86.75","MSD":"66","VS":"93.85"},"uses_additional_data":false},{"leaderboard":"/sota/medical-image-segmentation-on-hsvm","task":"Medical Image Segmentation","dataset":"HSVM","model":"MS-Dual-Guided","rank_in_archive_order":1,"of":1,"metrics":{"Dice Score":"83.2","MSD":"1.19","VS":"94.45"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1906.02849","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}