[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82508-en":3,"doc-seo-82508-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},82508,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1782698725881665579",7,"Healthcare","MedCAGD Context-Aware Gated Decoder for Efficient Medical Image Segmentation","Medical image segmentation depends on encoder–decoder architectures to convert rich feature representations into accurate pixel-level outputs under low contrast, structural ambiguity, and scale variability. While pretrained and transformer-based encoders improve feature extraction, segmentation quality remains limited by decoder design—especially cross-scale alignment, contextual integration, and boundary preservation. This work proposes a context-aware gated decoder that regulates feature fusion via lightweight multi-scale channel recalibration, gated skip fusion with spatial competition, and global context aggregation. Experiments on 11 benchmarks show consistent improvements over strong baselines with practical computation.","arXiv :2607 .00409v 1 [ cs .CV] 1 Jul 2026  \nMedCAGD: Context-Aware Gated Decoder for Efficient Medical Image Segmentation  \nSaad Wazir1, Patrick Dominique Vibild2, Dinh Phu Tran 1, Seongah Kim 1, and  \nDaeyoung Kim 1  \n1 School of Computing, Korea Advanced Institute of Science and Technology (KAIST),  \nDaejeon, Republic of Korea  \n{saad.wazir,phutx2000,kimsa0322,[kimd}@kaist.ac.kr](kimd}@kaist.ac.kr)  \n2 Department of Energy, Aalborg University, Aalborg, Denmark  \n[padovi@energy.aau.dk](padovi@energy.aau.dk)  \nAbstract. Medical image segmentation relies on the ability of encoder-decoder architectures to translate rich feature representations into accurate pixel-level predictions under challenging conditions such as low contrast, structural ambiguity, and scale variability. While recent advances in large-scale pretraining and transformer-based encoders have substantially improved feature extraction, segmentation accuracy remains constrained by decoder design, particularly in terms of cross-scale alignment, contextual integration, and boundary preservation. In this work, we revisit medical image segmentation from a decoder-centric perspective and propose a context-aware gated decoder that systematically regulates feature fusion and contextual aggregation throughout the decoding process. The proposed decoder integrates lightweight multi-scale channel recalibration, gated skip fusion with spatial competition and a global context aggregation mechanism that injects encoder-wide information into intermediate decoding stages. This design enables effective translation of strong pretrained encoder representations into spatially consistent predictions. Extensive experiments across 11 medical image segmentation benchmarks validate the effectiveness and demonstrate that the proposed approach consistently outperforms strong baselines while remaining computationally practical. Code: [https://github.com/saadwazir/MedCAGD](https://github.com/saadwazir/MedCAGD)  \n[Keywords:](Keywords: Medical Image Segmentation)[ Medical Image Segmentation](Keywords: Medical Image Segmentation) · Decoder Design · Bio-informatics  \n1 Introduction  \nMedical image segmentation is fundamental for quantitative analysis, diagnosis, treatment planning, and clinical assessment. Tasks such as organ delineation, lesion localization, tumor boundary extraction, and cellular segmentation require pixel-level precision under challenging conditions. To address these challenges, encoder–decoder architectures, particularly U-Net [40] based models, have become the dominant paradigm in medical image segmentation. Within this framework, performance is increasingly governed by encoder–decoder design, skip-connection formulation, feature fusion, and, more prominently, improvements in encoder capacity [38, 39, 44] . Although attention  \nAccepted at the European Conference on Computer Vision (ECCV 2026) .  \n2 S. Wazir et al.  \nmechanisms have evolved from local CNN-based modules to non-local and transformerbased formulations for long-range context modeling [34, 62], their computational cost and design limitations leave the integration of global context during decoding unresolved. Consequently, many segmentation errors arise from suboptimal decoding and cross-scale alignment rather than insufficient feature extraction [13, 37] .  \nRecently, foundation model approaches have demonstrated strong cross-domain generalization in vision tasks. In segmentation, SAM [24] has introduced a promptable, generalist paradigm, inspiring SAM-derived medical variants [3, 42, 61, 65] that demonstrate strong generalization. However, even medically adapted versions require substantial labeled data, modality specific supervision, and significant computational resources to approach the performance of specialist medical segmentation models [28] .  \nIn parallel, advances in large-scale pretraining have strengthened encoder representations [27, 46, 53, 54, 59], leading modern segmentation frameworks to adopt po","cbCaigRFMrdiyymb","https://ap.wps.com/l/cbCaigRFMrdiyymb","pdf",3574933,1,27,"English","en",105,"# Introduction\n## Decoder-centric perspective\n## Related approaches and motivation","[{\"question\":\"What problem does the proposed MedCAGD decoder address?\",\"answer\":\"It targets segmentation limitations caused by decoder design, especially cross-scale alignment, contextual integration, and boundary preservation, rather than relying only on stronger encoders.\"},{\"question\":\"How does MedCAGD regulate feature fusion during decoding?\",\"answer\":\"It uses lightweight multi-scale channel recalibration, gated skip fusion with spatial competition, and global context aggregation to inject encoder-wide information into intermediate decoding stages.\"},{\"question\":\"What evidence supports the effectiveness of MedCAGD?\",\"answer\":\"Extensive experiments across 11 medical image segmentation benchmarks demonstrate that the approach consistently outperforms strong baselines while remaining computationally 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problem does the proposed MedCAGD decoder address?","Question",{"text":74,"@type":75},"It targets segmentation limitations caused by decoder design, especially cross-scale alignment, contextual integration, and boundary preservation, rather than relying only on stronger encoders.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How does MedCAGD regulate feature fusion during decoding?",{"text":79,"@type":75},"It uses lightweight multi-scale channel recalibration, gated skip fusion with spatial competition, and global context aggregation to inject encoder-wide information into intermediate decoding stages.",{"name":81,"@type":72,"acceptedAnswer":82},"What evidence supports the effectiveness of MedCAGD?",{"text":83,"@type":75},"Extensive experiments across 11 medical image segmentation benchmarks demonstrate that the approach consistently outperforms strong baselines while remaining computationally 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