{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T18:15:48Z","timestamp":1778782548827,"version":"3.51.4"},"reference-count":71,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100015401","name":"Shaanxi Province Key Research and Development Projects","doi-asserted-by":"publisher","award":["2024CY2-GJHX-43"],"award-info":[{"award-number":["2024CY2-GJHX-43"]}],"id":[{"id":"10.13039\/501100015401","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Expert Systems with Applications"],"published-print":{"date-parts":[[2026,5]]},"DOI":"10.1016\/j.eswa.2026.131117","type":"journal-article","created":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T17:06:31Z","timestamp":1767719191000},"page":"131117","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":2,"special_numbering":"C","title":["A colon polyp segmentation network via collaborative decision-making of mixture of experts"],"prefix":"10.1016","volume":"308","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6441-4232","authenticated-orcid":false,"given":"Wenchao","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-7945-9804","authenticated-orcid":false,"given":"Wenhui","family":"Ye","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7204-3654","authenticated-orcid":false,"given":"Zhenhua","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-8698-0146","authenticated-orcid":false,"given":"Jianguo","family":"Ju","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.eswa.2026.131117_bib0001","series-title":"2025\u202fIEEE 38th international symposium on computer-based medical systems (CBMS)","first-page":"258","article-title":"Topo-VM-UNetv2: Encoding topology into vision mamba UNet for polyp segmentation","author":"Adame","year":"2025"},{"key":"10.1016\/j.eswa.2026.131117_bib0002","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.compmedimag.2015.02.007","article-title":"WM-DOVA maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians","volume":"43","author":"Bernal","year":"2015","journal-title":"Computerized Medical Imaging and Graphics"},{"key":"10.1016\/j.eswa.2026.131117_bib0003","unstructured":"Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A. L., & Zhou, Y. (2021). TransUNet: Transformers make strong encoders for medical image segmentation. arXiv: 2102.04306."},{"key":"10.1016\/j.eswa.2026.131117_bib0004","series-title":"Proceedings of the IEEE\/CVF international conference on computer vision","first-page":"17346","article-title":"AdaMV-MoE: Adaptive multi-task vision mixture-of-experts","author":"Chen","year":"2023"},{"key":"10.1016\/j.eswa.2026.131117_bib0005","unstructured":"Dai, D., Deng, C., Zhao, C., Xu, R. X., Gao, H., Chen, D., Li, J., Zeng, W., Yu, X., Wu, Y. et al. (2024). DeepSeekMoE: Towards ultimate expert specialization in mixture-of-experts language models. arXiv: 2401.06066."},{"key":"10.1016\/j.eswa.2026.131117_bib0006","unstructured":"Dong, B., Wang, W., Fan, D.-P., Li, J., Fu, H., & Shao, L. (2021). Polyp-PVT: Polyp segmentation with pyramid vision transformers. arXiv: 2108.06932."},{"key":"10.1016\/j.eswa.2026.131117_bib0007","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S. et al. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv: 2010.11929."},{"key":"10.1016\/j.eswa.2026.131117_bib0008","doi-asserted-by":"crossref","unstructured":"Dutta, T. K., Majhi, S., Nayak, D. R., & Jha, D. (2025a). Mamba guided boundary prior matters: A new perspective for generalized polyp segmentation. arXiv: 2507.01509.","DOI":"10.1007\/978-3-032-05141-7_37"},{"key":"10.1016\/j.eswa.2026.131117_bib0009","series-title":"2025\u202fIEEE\/CVF winter conference on applications of computer vision (WACV)","first-page":"4655","article-title":"SAM-Mamba: Mamba guided sam architecture for generalized zero-shot polyp segmentation","author":"Dutta","year":"2025"},{"key":"10.1016\/j.eswa.2026.131117_bib0010","series-title":"2017\u202fIEEE international conference on computer vision (ICCV)","first-page":"4558","article-title":"Structure-measure: A new way to evaluate foreground maps","author":"Fan","year":"2017"},{"key":"10.1016\/j.eswa.2026.131117_bib0011","series-title":"IJCAI","first-page":"698","article-title":"Enhanced-alignment measure for binary foreground map evaluation","author":"Fan","year":"2018"},{"key":"10.1016\/j.eswa.2026.131117_bib0012","article-title":"Cognitive vision inspired object segmentation metric and loss function","volume":"6","author":"Fan","year":"2021","journal-title":"Scientia Sinica Informationis"},{"key":"10.1016\/j.eswa.2026.131117_bib0013","series-title":"International conference on medical image computing and computer-assisted intervention","first-page":"263","article-title":"PraNet: Parallel reverse attention network for polyp segmentation","author":"Fan","year":"2020"},{"key":"10.1016\/j.eswa.2026.131117_bib0014","series-title":"European conference on computer vision","first-page":"367","article-title":"VP-SAM: Taming segment anything model for video polyp segmentation via disentanglement and spatio-temporal side network","author":"Fang","year":"2024"},{"issue":"4","key":"10.1016\/j.eswa.2026.131117_bib0015","doi-asserted-by":"crossref","first-page":"1378","DOI":"10.1007\/s11036-024-02411-y","article-title":"TMPSFormer: An efficient hybrid transformer-mlp network for polyp segmentation","volume":"29","author":"Guo","year":"2024","journal-title":"Mobile Networks and Applications"},{"key":"10.1016\/j.eswa.2026.131117_bib0016","unstructured":"Han, X., Wei, L., Dou, Z., Wang, Z., Qiang, C., He, X., Sun, Y., Han, Z., & Tian, Q. (2024). ViMoE: An empirical study of designing vision mixture-of-experts. arXiv: 2410.15732."},{"key":"10.1016\/j.eswa.2026.131117_bib0017","series-title":"Proceedings of the computer vision and pattern recognition conference","first-page":"25261","article-title":"MambaVision: A hybrid Mamba-transformer vision backbone","author":"Hatamizadeh","year":"2025"},{"key":"10.1016\/j.eswa.2026.131117_bib0018","series-title":"Proceedings of the IEEE international conference on computer vision","first-page":"2961","article-title":"Mask R-CNN","author":"He","year":"2017"},{"key":"10.1016\/j.eswa.2026.131117_bib0019","series-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","first-page":"7132","article-title":"Squeeze-and-excitation networks","author":"Hu","year":"2018"},{"key":"10.1016\/j.eswa.2026.131117_bib0020","unstructured":"Huang, C.-H., Wu, H.-Y., & Lin, Y.-L. (2021). HarDNet-MSEG: A simple encoder-decoder polyp segmentation neural network that achieves over 0.9 mean dice and 86 FPS. arXiv: 2101.07172."},{"key":"10.1016\/j.eswa.2026.131117_bib0021","first-page":"269","article-title":"Tutel: Adaptive mixture-of-experts at scale","volume":"5","author":"Hwang","year":"2023","journal-title":"Proceedings of Machine Learning and Systems"},{"key":"10.1016\/j.eswa.2026.131117_bib0022","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2024.108962","article-title":"Rethinking encoder-decoder architecture using vision transformer for colorectal polyp and surgical instruments segmentation","volume":"136","author":"Iqbal","year":"2024","journal-title":"Engineering Applications of Artificial Intelligence"},{"key":"10.1016\/j.eswa.2026.131117_bib0023","series-title":"International conference on multimedia modeling","first-page":"451","article-title":"Kvasir-SEG: A segmented polyp dataset","author":"Jha","year":"2020"},{"key":"10.1016\/j.eswa.2026.131117_bib0024","doi-asserted-by":"crossref","DOI":"10.1016\/j.neunet.2025.107553","article-title":"Modeling multi-scale uncertainty with evidence integration for reliable polyp segmentation","volume":"189","author":"Kang","year":"2025","journal-title":"Neural Networks"},{"key":"10.1016\/j.eswa.2026.131117_bib0025","series-title":"Proceedings of the IEEE\/CVF international conference on computer vision","first-page":"4015","article-title":"Segment anything","author":"Kirillov","year":"2023"},{"key":"10.1016\/j.eswa.2026.131117_bib0026","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2024.106336","article-title":"PRCNet: A parallel reverse convolutional attention network for colorectal polyp segmentation","volume":"95","author":"Li","year":"2024","journal-title":"Biomedical Signal Processing and Control"},{"key":"10.1016\/j.eswa.2026.131117_bib0027","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.125419","article-title":"FMCA-Net: A feature secondary multiplexing and dilated convolutional attention polyp segmentation network based on pyramid vision transformer","volume":"260","author":"Li","year":"2025","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2026.131117_bib0028","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.112050","article-title":"DMSA-UNet: Dual multi-scale attention makes unet more strong for medical image segmentation","volume":"299","author":"Li","year":"2024","journal-title":"Knowledge-Based Systems"},{"key":"10.1016\/j.eswa.2026.131117_bib0029","doi-asserted-by":"crossref","first-page":"6754","DOI":"10.1109\/JBHI.2025.3555805","article-title":"Multi-scale dynamic sparse attention UNet for medical image segmentation","volume":"29","author":"Li","year":"2025","journal-title":"IEEE Journal of Biomedical and Health Informatics"},{"key":"10.1016\/j.eswa.2026.131117_bib0030","first-page":"1","article-title":"DS-TransUNet: Dual swin transformer u-net for medical image segmentation","volume":"71","author":"Lin","year":"2022","journal-title":"IEEE Transactions on Instrumentation and Measurement"},{"key":"10.1016\/j.eswa.2026.131117_bib0031","unstructured":"Lin, B., Tang, Z., Ye, Y., Cui, J., Zhu, B., Jin, P., Huang, J., Zhang, J., Pang, Y., Ning, M. et al. (2024a). MoE-LLaVA: Mixture of experts for large vision-language models. arXiv: 2401.15947."},{"key":"10.1016\/j.eswa.2026.131117_bib0032","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.112181","article-title":"Polyp-LVT: Polyp segmentation with lightweight vision transformers","volume":"300","author":"Lin","year":"2024","journal-title":"Knowledge-Based Systems"},{"key":"10.1016\/j.eswa.2026.131117_bib0033","article-title":"CAFE-Net: Cross-attention and feature exploration network for polyp segmentation","volume":"238","author":"Liu","year":"2024","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2026.131117_bib0034","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2023.105792","article-title":"Attention combined pyramid vision transformer for polyp segmentation","volume":"89","author":"Liu","year":"2024","journal-title":"Biomedical Signal Processing and Control"},{"issue":"7","key":"10.1016\/j.eswa.2026.131117_bib0035","doi-asserted-by":"crossref","first-page":"5414","DOI":"10.1109\/TCSVT.2023.3348598","article-title":"The devil is in the boundary: Boundary-enhanced polyp segmentation","volume":"34","author":"Liu","year":"2024","journal-title":"IEEE Transactions on Circuits and Systems for Video Technology"},{"key":"10.1016\/j.eswa.2026.131117_bib0036","doi-asserted-by":"crossref","unstructured":"Mansoori, M., Shahabodini, S., Abouei, J., Plataniotis, K. N., & Mohammadi, A. (2024). Polyp SAM 2: Advancing zero shot polyp segmentation in colorectal cancer detection. arXiv: 2408.05892.","DOI":"10.1109\/ICHMS65439.2025.11154309"},{"key":"10.1016\/j.eswa.2026.131117_bib0037","series-title":"2014\u202fIEEE conference on computer vision and pattern recognition","first-page":"248","article-title":"How to evaluate foreground maps","author":"Margolin","year":"2014"},{"key":"10.1016\/j.eswa.2026.131117_bib0038","series-title":"2016 fourth international conference on 3D vision (3DV)","first-page":"565","article-title":"V-Net: Fully convolutional neural networks for volumetric medical image segmentation","author":"Milletari","year":"2016"},{"key":"10.1016\/j.eswa.2026.131117_bib0039","unstructured":"Muennighoff, N., Soldaini, L., Groeneveld, D., Lo, K., Morrison, J., Min, S., Shi, W., Walsh, P., Tafjord, O., Lambert, N. et al. (2024). OLMoE: Open mixture-of-experts language models. arXiv: 2409.02060."},{"key":"10.1016\/j.eswa.2026.131117_bib0040","series-title":"Proceedings of the 4th international conference on computer, artificial intelligence and control engineering","first-page":"796","article-title":"Contextual-SAM: Segment anything model with contextual representation for polyp segmentation","author":"Qu","year":"2025"},{"key":"10.1016\/j.eswa.2026.131117_bib0041","series-title":"International conference on machine learning","first-page":"8748","article-title":"Learning transferable visual models from natural language supervision","author":"Radford","year":"2021"},{"key":"10.1016\/j.eswa.2026.131117_bib0042","series-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","first-page":"4989","article-title":"PP-SAM: Perturbed prompts for robust adaption of segment anything model for polyp segmentation","author":"Rahman","year":"2024"},{"key":"10.1016\/j.eswa.2026.131117_bib0043","series-title":"2025\u202fIEEE\/CVF winter conference on applications of computer vision (WACV)","first-page":"3172","article-title":"Personalized mixture of experts for multi-site medical image segmentation","author":"Rahman","year":"2025"},{"key":"10.1016\/j.eswa.2026.131117_bib0044","series-title":"International conference on medical image computing and computer-assisted intervention","first-page":"234","article-title":"U-Net: Convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015"},{"key":"10.1016\/j.eswa.2026.131117_bib0045","doi-asserted-by":"crossref","first-page":"4731","DOI":"10.1609\/aaai.v38i5.28274","article-title":"Polyper: Boundary sensitive polyp segmentation","volume":"vol. 38","author":"Shao","year":"2024","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"10.1016\/j.eswa.2026.131117_bib0046","doi-asserted-by":"crossref","unstructured":"Shen, S., Yao, Z., Li, C., Darrell, T., Keutzer, K., & He, Y. (2023). Scaling vision-language models with sparse mixture of experts. arXiv: 2303.07226.","DOI":"10.18653\/v1\/2023.findings-emnlp.758"},{"issue":"2","key":"10.1016\/j.eswa.2026.131117_bib0047","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1007\/s11548-013-0926-3","article-title":"Toward embedded detection of polyps in wce images for early diagnosis of colorectal cancer","volume":"9","author":"Silva","year":"2014","journal-title":"International Journal of Computer Assisted Radiology and Surgery"},{"key":"10.1016\/j.eswa.2026.131117_bib0048","series-title":"2022 26th international conference on pattern recognition (ICPR)","first-page":"4321","article-title":"GMSRF-Net: An improved generalizability with global multi-scale residual fusion network for polyp segmentation","author":"Srivastava","year":"2022"},{"key":"10.1016\/j.eswa.2026.131117_bib0049","series-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","first-page":"1","article-title":"Going deeper with convolutions","author":"Szegedy","year":"2015"},{"issue":"2","key":"10.1016\/j.eswa.2026.131117_bib0050","doi-asserted-by":"crossref","first-page":"630","DOI":"10.1109\/TMI.2015.2487997","article-title":"Automated polyp detection in colonoscopy videos using shape and context information","volume":"35","author":"Tajbakhsh","year":"2016","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"10.1016\/j.eswa.2026.131117_bib0051","doi-asserted-by":"crossref","DOI":"10.1155\/2017\/4037190","article-title":"A benchmark for endoluminal scene segmentation of colonoscopy images","volume":"2017","author":"V\u00e1zquez","year":"2017","journal-title":"Journal of Healthcare Engineering"},{"key":"10.1016\/j.eswa.2026.131117_bib0052","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2024.103449","article-title":"Dynamic spectrum-driven hierarchical learning network for polyp segmentation","volume":"101","author":"Wang","year":"2025","journal-title":"Medical Image Analysis"},{"key":"10.1016\/j.eswa.2026.131117_bib0053","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s00521-025-11234-1","article-title":"Conducting patch contrastive learning with mixture of experts on mixed datasets for medical image segmentation","volume":"37","author":"Wang","year":"2025","journal-title":"Neural Computing and Applications"},{"key":"10.1016\/j.eswa.2026.131117_bib0054","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.127558","article-title":"Multi-feature fusion for accurate polyp segmentation using pyramid visual transformers","volume":"280","author":"Wang","year":"2025","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2026.131117_bib0055","series-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","first-page":"11534","article-title":"ECA-Net: Efficient channel attention for deep convolutional neural networks","author":"Wang","year":"2020"},{"issue":"3","key":"10.1016\/j.eswa.2026.131117_bib0056","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1007\/s41095-022-0274-8","article-title":"PVT V2: Improved baselines with pyramid vision transformer","volume":"8","author":"Wang","year":"2022","journal-title":"Computational Visual Media"},{"key":"10.1016\/j.eswa.2026.131117_bib0057","series-title":"Proceedings of the European conference on computer vision (ECCV)","first-page":"3","article-title":"CBAM: Convolutional block attention module","author":"Woo","year":"2018"},{"key":"10.1016\/j.eswa.2026.131117_bib0058","unstructured":"Wu, Z., Chen, X., Pan, Z., Liu, X., Liu, W., Dai, D., Gao, H., Ma, Y., Wu, C., Wang, B. et al. (2024a). DeepSeek-VL2: Mixture-of-experts vision-language models for advanced multimodal understanding. arXiv: 2412.10302."},{"key":"10.1016\/j.eswa.2026.131117_bib0059","series-title":"Proceedings of the 6th ACM international conference on multimedia in asia","first-page":"1","article-title":"MoE-polyp: Shifting more attention to small polyp segmentation via mixture-of-experts","author":"Wu","year":"2024"},{"key":"10.1016\/j.eswa.2026.131117_bib0060","unstructured":"Xie, J., Liao, R., Zhang, Z., Yi, S., Zhu, Y., & Luo, G. (2024). ProMamba: Prompt-Mamba for polyp segmentation. arXiv: 2403.13660."},{"key":"10.1016\/j.eswa.2026.131117_bib0061","series-title":"Proceedings of the computer vision and pattern recognition conference","first-page":"10203","article-title":"dFLMoe: Decentralized federated learning via mixture of experts for medical data analysis","author":"Xie","year":"2025"},{"key":"10.1016\/j.eswa.2026.131117_bib0062","series-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","first-page":"1492","article-title":"Aggregated residual transformations for deep neural networks","author":"Xie","year":"2017"},{"key":"10.1016\/j.eswa.2026.131117_bib0063","doi-asserted-by":"crossref","first-page":"954","DOI":"10.1109\/LSP.2024.3378106","article-title":"Boundary refinement network for colorectal polyp segmentation in colonoscopy images","volume":"31","author":"Yue","year":"2024","journal-title":"IEEE Signal Processing Letters"},{"key":"10.1016\/j.eswa.2026.131117_bib0064","first-page":"98782","article-title":"Flex-MoE: Modeling arbitrary modality combination via the flexible mixture-of-experts","volume":"37","author":"Yun","year":"2024","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.eswa.2026.131117_bib0065","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2022.106173","article-title":"HSNet: A hybrid semantic network for polyp segmentation","volume":"150","author":"Zhang","year":"2022","journal-title":"Computers in Biology and Medicine"},{"key":"10.1016\/j.eswa.2026.131117_bib0066","unstructured":"Zhao, X., Ding, W., An, Y., Du, Y., Yu, T., Li, M., Tang, M., & Wang, J. (2023). Fast segment anything. arXiv: 2306.12156."},{"key":"10.1016\/j.eswa.2026.131117_bib0067","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2025.113701","article-title":"Weakpolyp-SAM: Segment anything model-driven weakly-supervised polyp segmentation","volume":"322","author":"Zhao","year":"2025","journal-title":"Knowledge-Based Systems"},{"key":"10.1016\/j.eswa.2026.131117_bib0068","series-title":"International conference on medical image computing and computer-assisted intervention","first-page":"711","article-title":"TextPolyp: Point-supervised polyp segmentation with text cues","author":"Zhao","year":"2024"},{"key":"10.1016\/j.eswa.2026.131117_bib0069","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2023.109555","article-title":"Cross-level feature aggregation network for polyp segmentation","volume":"140","author":"Zhou","year":"2023","journal-title":"Pattern Recognition"},{"key":"10.1016\/j.eswa.2026.131117_bib0070","series-title":"Deep learning in medical image analysis and multimodal learning for clinical decision support","first-page":"3","article-title":"UNet++: A nested U-Net architecture for medical image segmentation","author":"Zhou","year":"2018"},{"key":"10.1016\/j.eswa.2026.131117_bib0071","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2024.102759","article-title":"Polyp-Mamba: A hybrid multi-frequency perception gated selection network for polyp segmentation","volume":"115","author":"Zhu","year":"2025","journal-title":"Information Fusion"}],"container-title":["Expert Systems with Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S095741742600031X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S095741742600031X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T17:48:37Z","timestamp":1778780917000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S095741742600031X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5]]},"references-count":71,"alternative-id":["S095741742600031X"],"URL":"https:\/\/doi.org\/10.1016\/j.eswa.2026.131117","relation":{},"ISSN":["0957-4174"],"issn-type":[{"value":"0957-4174","type":"print"}],"subject":[],"published":{"date-parts":[[2026,5]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A colon polyp segmentation network via collaborative decision-making of mixture of experts","name":"articletitle","label":"Article Title"},{"value":"Expert Systems with Applications","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.eswa.2026.131117","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"131117"}}