{"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/tganet-text-guided-attention-for-improved","title":"TGANet: Text-guided attention for improved polyp segmentation","arxiv_id":"2205.04280","date":"2022-05-09","proceeding":null,"authors":["Nikhil Kumar Tomar","Debesh Jha","Ulas Bagci","Sharib Ali"],"abstract":"Colonoscopy is a gold standard procedure but is highly operator-dependent. Automated polyp segmentation, a precancerous precursor, can minimize missed rates and timely treatment of colon cancer at an early stage. Even though there are deep learning methods developed for this task, variability in polyp size can impact model training, thereby limiting it to the size attribute of the majority of samples in the training dataset that may provide sub-optimal results to differently sized polyps. In this work, we exploit size-related and polyp number-related features in the form of text attention during training. We introduce an auxiliary classification task to weight the text-based embedding that allows network to learn additional feature representations that can distinctly adapt to differently sized polyps and can adapt to cases with multiple polyps. Our experimental results demonstrate that these added text embeddings improve the overall performance of the model compared to state-of-the-art segmentation methods. We explore four different datasets and provide insights for size-specific improvements. Our proposed text-guided attention network (TGANet) can generalize well to variable-sized polyps in different datasets.","url_abs":"https://arxiv.org/abs/2205.04280v1","url_pdf":"https://arxiv.org/pdf/2205.04280v1.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":"tganet-text-guided-attention-for-improved","repo_url":"https://github.com/nikhilroxtomar/tganet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"attribute","task_name":"Attribute"},{"task_slug":"medical-image-segmentation","task_name":"Medical Image Segmentation"},{"task_slug":"polyp-segmentation","task_name":"Polyp Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/medical-image-segmentation-on-bkai-igh","task":"Medical Image Segmentation","dataset":"BKAI-IGH NeoPolyp-Small","model":"TGANet","rank_in_archive_order":4,"of":9,"metrics":{"Average Dice":"0.9023","mIoU":"0.8409"},"uses_additional_data":false},{"leaderboard":"/sota/medical-image-segmentation-on-kvasir-seg","task":"Medical Image Segmentation","dataset":"Kvasir-SEG","model":"TGA-Net","rank_in_archive_order":43,"of":58,"metrics":{"mIoU":"0.8330","mean Dice":"0.8982"},"uses_additional_data":false},{"leaderboard":"/sota/polyp-segmentation-on-kvasir-seg","task":"Polyp Segmentation","dataset":"Kvasir-SEG","model":"TGA-Net","rank_in_archive_order":3,"of":8,"metrics":{"mDice":"0.8982","mIoU":"0.8330"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2205.04280","atlas_url":"https://app.syntology.ai/?focus=2205.04280","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}