{"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/from-semantic-segmentation-of-natural-images","title":"From Semantic Segmentation of Natural Images to Medical Image Segmentation Using ViT-Based Architectures","arxiv_id":null,"date":"2025-01-31","proceeding":"Structural, Syntactic, and Statistical Pattern Recognition 2025 1","authors":["Alexandru Valentin Patrascu","Ciprian-Mihai Ceausescu","and Bogdan Alexe"],"abstract":"We address the problem of medical image segmentation in the context of limited training data. Our approach builds on the capabilities of the Vision Transformer (ViT) and the recent Segmenter model, adapting them for the task of medical image segmentation. By leveraging Segmenter models pre-trained on moderately-sized datasets like ADE20K, we demonstrate their effectiveness when fine-tuned on smaller and scarce medical imaging datasets, specifically those for skin lesions and polyps. Employing our proposed training strategy, the adapted Segmenter model both matches and surpasses the current state-of-the-art on three key medical image datasets: ISIC2018 for skin lesions, and CVCClinicDB and ETIS-LaribPolypDB for polyps, while maintaining competitive performance on Kvasir-SEG and CVC ColonDB.","url_abs":"https://link.springer.com/chapter/10.1007/978-3-031-80507-3_12","url_pdf":"https://link.springer.com/chapter/10.1007/978-3-031-80507-3_12","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":[],"tasks":[{"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":"absolute-position-encodings","method_name":"Absolute Position Encodings"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"label-smoothing","method_name":"Label Smoothing"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"transformer","method_name":"Transformer"},{"method_slug":"vision-transformer","method_name":"Vision Transformer"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/lesion-segmentation-on-isic-2018","task":"Lesion Segmentation","dataset":"ISIC 2018","model":"SegMed","rank_in_archive_order":6,"of":17,"metrics":{"Mean IoU":"0.841","mean Dice":"0.911"},"uses_additional_data":false},{"leaderboard":"/sota/medical-image-segmentation-on-cvc-clinicdb","task":"Medical Image Segmentation","dataset":"CVC-ClinicDB","model":"SegMed","rank_in_archive_order":10,"of":48,"metrics":{"mIoU":"0.902","mean Dice":"0.948"},"uses_additional_data":false},{"leaderboard":"/sota/medical-image-segmentation-on-cvc-colondb","task":"Medical Image Segmentation","dataset":"CVC-ColonDB","model":"SegMed","rank_in_archive_order":4,"of":25,"metrics":{"mIoU":"0.854","mean Dice":"0.921"},"uses_additional_data":false},{"leaderboard":"/sota/medical-image-segmentation-on-etis","task":"Medical Image Segmentation","dataset":"ETIS-LARIBPOLYPDB","model":"SegMed","rank_in_archive_order":2,"of":25,"metrics":{"mIoU":"0.879","mean Dice":"0.936"},"uses_additional_data":false},{"leaderboard":"/sota/medical-image-segmentation-on-kvasir-seg","task":"Medical Image Segmentation","dataset":"Kvasir-SEG","model":"SegMed","rank_in_archive_order":4,"of":58,"metrics":{"mIoU":"0.899","mean Dice":"0.947"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}