{"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/rethinking-the-nested-u-net-approach","title":"Rethinking the Nested U-Net Approach: Enhancing Biomarker Segmentation with Attention Mechanisms and Multiscale Feature Fusion","arxiv_id":"2504.06158","date":"2025-04-08","proceeding":null,"authors":["Saad Wazir","Daeyoung Kim"],"abstract":"Identifying biomarkers in medical images is vital for a wide range of biotech applications. However, recent Transformer and CNN based methods often struggle with variations in morphology and staining, which limits their feature extraction capabilities. In medical image segmentation, where data samples are often limited, state-of-the-art (SOTA) methods improve accuracy by using pre-trained encoders, while end-to-end approaches typically fall short due to difficulties in transferring multiscale features effectively between encoders and decoders. To handle these challenges, we introduce a nested UNet architecture that captures both local and global context through Multiscale Feature Fusion and Attention Mechanisms. This design improves feature integration from encoders, highlights key channels and regions, and restores spatial details to enhance segmentation performance. Our method surpasses SOTA approaches, as evidenced by experiments across four datasets and detailed ablation studies. Code: https://github.com/saadwazir/ReN-UNet","url_abs":"https://arxiv.org/abs/2504.06158v1","url_pdf":"https://arxiv.org/pdf/2504.06158v1.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":"rethinking-the-nested-u-net-approach","repo_url":"https://github.com/saadwazir/ReN-UNet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"2d-semantic-segmentation","task_name":"2D Semantic Segmentation"},{"task_slug":"image-segmentation","task_name":"Image 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":"adam","method_name":"Adam"},{"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":"position-wise-feed-forward-layer","method_name":"Position-Wise Feed-Forward Layer"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"transformer","method_name":"Transformer"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/medical-image-segmentation-on-2018-data","task":"Medical Image Segmentation","dataset":"2018 Data Science Bowl","model":"ReN-UNet","rank_in_archive_order":1,"of":10,"metrics":{"AHD95":"6.5914","ASD":"1.7074","Dice":"92.79","mIoU":"87.22"},"uses_additional_data":false},{"leaderboard":"/sota/medical-image-segmentation-on-electron","task":"Medical Image Segmentation","dataset":"Electron Microscopy Dataset","model":"ReN-UNet","rank_in_archive_order":1,"of":1,"metrics":{"AHD95":"5.3703","ASD":"0.3047","Dice":"93.55","IoU":"87.93"},"uses_additional_data":false},{"leaderboard":"/sota/medical-image-segmentation-on-monuseg","task":"Medical Image Segmentation","dataset":"MoNuSeg","model":"ReN-UNet","rank_in_archive_order":2,"of":15,"metrics":{"AHD95":"2.2422","ASD":"0.1583","F1":"84.12","IoU":"73.06"},"uses_additional_data":false},{"leaderboard":"/sota/medical-image-segmentation-on-tnbc","task":"Medical Image Segmentation","dataset":"TNBC","model":"ReN-UNet","rank_in_archive_order":1,"of":1,"metrics":{"AHD95":"10.355","Dice":"78.99","IoU":"66.13"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}