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To address these challenges, we propose MamNet-PT, a hybrid segmentation architecture that integrates efficient long-range dependency modeling, multi-resolution feature aggregation, and uncertainty-aware prediction within a unified framework. First, a selective state-space model is embedded into the U-Net-based feature pathway to capture long-range spatial dependencies with linear computational complexity, which is particularly important for irregular and spatially extended tumor regions. Second, a pre-trained ResNet-50 encoder is used to improve feature robustness under limited annotated medical data. Third, a gated feature interaction mechanism adaptively balances Mamba-derived global contextual features and CNN-derived local boundary features, avoiding simple feature concatenation or uncontrolled module stacking. In addition, a multi-resolution pyramid fusion module strengthens scale-aware representation of small enhancing foci and extensive edema, while Monte Carlo Dropout-based uncertainty estimation provides spatial confidence maps for retrospective confidence characterization and failure-mode analysis. On the BraTS2020 benchmark, MamNet-PT achieves a Dice score of 96.7% and an Intersection over Union of 95.4%, outperforming representative CNN-Transformer and Mamba-based segmentation baselines. Ablation experiments further confirm that the performance gain is attributable to the complementary effects of selective state-space modeling, gated global-local fusion, multi-resolution aggregation, and uncertainty-aware inference. These results suggest that MamNet-PT is a promising research framework for accurate and efficient brain tumor segmentation under retrospective benchmark evaluation.<\/jats:p>","DOI":"10.1371\/journal.pone.0351667","type":"journal-article","created":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T17:43:58Z","timestamp":1784137438000},"page":"e0351667","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":1,"title":["MamNet-PT: A Mamba-enhanced hybrid architecture with selective state-space modeling for uncertainty-aware brain tumor segmentation"],"prefix":"10.1371","volume":"21","author":[{"given":"Yu","family":"Sun","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-3537-6787","authenticated-orcid":true,"given":"Yihang","family":"Qin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,7,15]]},"reference":[{"key":"pone.0351667.ref001","doi-asserted-by":"crossref","first-page":"82031","DOI":"10.1109\/ACCESS.2021.3086020","article-title":"U-net and its variants for medical image segmentation: A review of theory and applications","volume":"9","author":"N Siddique","year":"2021","journal-title":"IEEE Access"},{"key":"pone.0351667.ref002","first-page":"234","volume-title":"U-net: Convolutional networks for biomedical image segmentation. 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Shifting more attention to visual backbone: Query-modulated refinement networks for end-to-end visual grounding. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2022. 15502\u201312.","DOI":"10.1109\/CVPR52688.2022.01506"},{"key":"pone.0351667.ref007","doi-asserted-by":"crossref","unstructured":"Bhattarai M, Oyen D, Castorena J. Diagram image retrieval using sketch-based deep learning and transfer learning. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition workshops, 2020. 174\u20135.","DOI":"10.1109\/CVPRW50498.2020.00095"},{"issue":"2","key":"pone.0351667.ref008","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1007\/s11548-022-02738-5","article-title":"Multi-scale feature pyramid fusion network for medical image segmentation","volume":"18","author":"B Zhang","year":"2023","journal-title":"Int J Comput Assist Radiol Surg"},{"issue":"2","key":"pone.0351667.ref009","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1080\/1463922X.2016.1166405","article-title":"A generic framework of intelligent adaptive learning systems: From learning effectiveness to training transfer","volume":"18","author":"M Hou","year":"2016","journal-title":"Theoretical Issues in Ergonomics Science"},{"issue":"1","key":"pone.0351667.ref010","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1002\/mrm.29624","article-title":"Bayesian MRI reconstruction with joint uncertainty estimation using diffusion models","volume":"90","author":"G Luo","year":"2023","journal-title":"Magn Reson Med"},{"issue":"3","key":"pone.0351667.ref011","article-title":"MLGT: A multimodal graph attention network for virtual screening of anti-Uveitis drugs","volume":"21","author":"Y Sun","year":"2026","journal-title":"PLoS One"},{"issue":"2","key":"pone.0351667.ref012","article-title":"Game-theoretical evaluating rules for minimizing losses under multi-objective situations","volume":"33","author":"HC Wei","year":"2025","journal-title":"Engineering Letters"},{"key":"pone.0351667.ref013","doi-asserted-by":"crossref","unstructured":"Aupetit M, Sedlmair M, Abbas MM. Toward perception-based evaluation of clustering techniques for visual analytics. In: 2019 IEEE Visualization Conference (VIS), 2019. 141\u20135.","DOI":"10.1109\/VISUAL.2019.8933620"},{"key":"pone.0351667.ref014","doi-asserted-by":"crossref","first-page":"950706","DOI":"10.3389\/fonc.2022.950706","article-title":"MM-UNet: A multimodality brain tumor segmentation network in MRI images","volume":"12","author":"L Zhao","year":"2022","journal-title":"Front Oncol"},{"key":"pone.0351667.ref015","doi-asserted-by":"crossref","first-page":"110140","DOI":"10.1016\/j.patcog.2023.110140","article-title":"CS-net: Conv-simpleformer network for agricultural image segmentation","volume":"147","author":"L Liu","year":"2024","journal-title":"Pattern Recognition"},{"key":"pone.0351667.ref016","doi-asserted-by":"crossref","first-page":"109297","DOI":"10.1016\/j.asoc.2022.109297","article-title":"DDU-Net: A dual dense U-structure network for medical image segmentation","volume":"126","author":"J Cheng","year":"2022","journal-title":"Applied Soft Computing"},{"key":"pone.0351667.ref017","doi-asserted-by":"crossref","first-page":"111829","DOI":"10.1016\/j.engappai.2025.111829","article-title":"A cross-dimensional synergistic network for brain tumor segmentation","volume":"160","author":"C-W Lin","year":"2025","journal-title":"Engineering Applications of Artificial Intelligence"},{"issue":"3","key":"pone.0351667.ref018","doi-asserted-by":"crossref","first-page":"337","DOI":"10.12677\/mos.2025.143227","article-title":"SAM2U-Net: Medical imaging segmentation model based on SAM2 and U-Net","volume":"14","author":"i Hou J u n w e","year":"2025","journal-title":"Modeling and Simulation"},{"key":"pone.0351667.ref019","doi-asserted-by":"crossref","first-page":"105534","DOI":"10.1016\/j.bspc.2023.105534","article-title":"HiFuse: Hierarchical multi-scale feature fusion network for medical image classification","volume":"87","author":"X Huo","year":"2024","journal-title":"Biomedical Signal Processing and Control"},{"key":"pone.0351667.ref020","doi-asserted-by":"crossref","unstructured":"He Y, Guo P, Tang Y, et al. VISTA3D: A unified segmentation foundation model for 3D medical imaging. In: Proceedings of the Computer Vision and Pattern Recognition Conference, 2025. 20863\u201373.","DOI":"10.1109\/CVPR52734.2025.01943"},{"key":"pone.0351667.ref021","doi-asserted-by":"crossref","first-page":"131016","DOI":"10.1016\/j.neucom.2025.131016","article-title":"ConvMamba: Combining Mamba with CNN for hyperspectral image classification","volume":"652","author":"H Zhang","year":"2025","journal-title":"Neurocomputing"},{"issue":"1","key":"pone.0351667.ref022","doi-asserted-by":"crossref","first-page":"15013","DOI":"10.1038\/s41598-024-64982-w","article-title":"Multi-branch CNN and grouping cascade attention for medical image classification","volume":"14","author":"S Liu","year":"2024","journal-title":"Sci Rep"},{"key":"pone.0351667.ref023","doi-asserted-by":"crossref","first-page":"103287","DOI":"10.1016\/j.artmed.2025.103287","article-title":"Multi-modal medical image synthesis via dual-branch wavelet encoding and deformable feature interaction","volume":"171","author":"X Jia","year":"2026","journal-title":"Artif Intell Med"},{"issue":"10","key":"pone.0351667.ref024","doi-asserted-by":"crossref","DOI":"10.1007\/s10462-024-10922-z","article-title":"Domain generalization through meta-learning: A survey","volume":"57","author":"AG Khoee","year":"2024","journal-title":"Artif Intell Rev"},{"key":"pone.0351667.ref025","doi-asserted-by":"crossref","first-page":"102666","DOI":"10.1016\/j.compmedimag.2025.102666","article-title":"DuetMatch: Harmonizing semi-supervised brain MRI segmentation via decoupled branch optimization","volume":"126","author":"T-H Nguyen","year":"2025","journal-title":"Comput Med Imaging Graph"},{"key":"pone.0351667.ref026","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2023.3265650","article-title":"MSH-Net: Modality-shared hallucination with joint adaptation distillation for remote sensing image classification using missing modalities","volume":"61","author":"S Wei","year":"2023","journal-title":"IEEE Trans Geosci Remote Sensing"},{"key":"pone.0351667.ref027","article-title":"Yolov13: Real-time object detection with hypergraph-enhanced adaptive visual perception","author":"M Lei","year":"2025","journal-title":"arXiv preprint"},{"key":"pone.0351667.ref028","unstructured":"Sapkota R, Cheppally RH, Sharda A, et al. YOLO26: key architectural enhancements and performance benchmarking for real-time object detection, 2025."},{"key":"pone.0351667.ref029","article-title":"YOLOE-26: Integrating YOLO26 with YOLOE for real-time open-vocabulary instance segmentation","author":"R Sapkota","year":"2026","journal-title":"arXiv preprint"},{"key":"pone.0351667.ref030","article-title":"Graph-mamba: Towards long-range graph sequence modeling with selective state spaces","author":"C Wang","year":"2024","journal-title":"arXiv preprint"},{"key":"pone.0351667.ref031","doi-asserted-by":"crossref","first-page":"108436","DOI":"10.1016\/j.compeleceng.2022.108436","article-title":"Pyramid attention object detection network with multi-scale feature fusion","volume":"104","author":"X Chen","year":"2022","journal-title":"Computers and Electrical Engineering"},{"key":"pone.0351667.ref032","doi-asserted-by":"crossref","unstructured":"Cheng M-M, Warrell J, Lin W-Y, Zheng S, Vineet V, Crook N. Efficient Salient Region Detection with Soft Image Abstraction. In: 2013 IEEE International Conference on Computer Vision, 2013. 1529\u201336. https:\/\/doi.org\/10.1109\/iccv.2013.193","DOI":"10.1109\/ICCV.2013.193"},{"key":"pone.0351667.ref033","doi-asserted-by":"crossref","unstructured":"Wang Z, Cun X, Bao J. Uformer: A general u-shaped transformer for image restoration. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, 2022. 17683\u201393.","DOI":"10.1109\/CVPR52688.2022.01716"},{"key":"pone.0351667.ref034","doi-asserted-by":"crossref","first-page":"104442","DOI":"10.1016\/j.imavis.2022.104442","article-title":"Multi-scale feature aggregation and boundary awareness network for salient object detection","volume":"122","author":"Q Wu","year":"2022","journal-title":"Image and Vision Computing"},{"key":"pone.0351667.ref035","doi-asserted-by":"crossref","first-page":"36538","DOI":"10.1109\/ACCESS.2022.3163384","article-title":"A Review on bayesian deep learning in healthcare: Applications and challenges","volume":"10","author":"AA Abdullah","year":"2022","journal-title":"IEEE Access"}],"container-title":["PLOS One"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0351667","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T17:44:07Z","timestamp":1784137447000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0351667"}},"subtitle":[],"editor":[{"given":"Amgad","family":"Muneer","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2026,7,15]]},"references-count":35,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2026,7,15]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0351667","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,15]]}}}