{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T20:50:18Z","timestamp":1758055818760,"version":"3.44.0"},"reference-count":61,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2025,6,10]],"date-time":"2025-06-10T00:00:00Z","timestamp":1749513600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,6,10]],"date-time":"2025-06-10T00:00:00Z","timestamp":1749513600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Aeronautical Science Foundation of China","award":["No. 2024Z071075001","No. 2024Z071075001","No. 2024Z071075001","No. 2024Z071075001"],"award-info":[{"award-number":["No. 2024Z071075001","No. 2024Z071075001","No. 2024Z071075001","No. 2024Z071075001"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Vis Comput"],"published-print":{"date-parts":[[2025,9]]},"DOI":"10.1007\/s00371-025-04027-z","type":"journal-article","created":{"date-parts":[[2025,6,10]],"date-time":"2025-06-10T06:59:44Z","timestamp":1749538784000},"page":"10171-10187","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Edge-awareness and feature decoupling enhancement network for camouflaged object detection"],"prefix":"10.1007","volume":"41","author":[{"given":"Tao","family":"Xiang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinfu","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shu","family":"Cai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinglei","family":"Bai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,6,10]]},"reference":[{"key":"4027_CR1","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1109\/TIP.2021.3130490","volume":"31","author":"T-N Le","year":"2021","unstructured":"Le, T.-N., Cao, Y., Nguyen, T.-C., Le, M.-Q., Nguyen, K.-D., Do, T.-T., Tran, M.-T., Nguyen, T.V.: Camouflaged instance segmentation in-the-wild: dataset, method, and benchmark suite. IEEE Trans. Image Process 31, 287\u2013300 (2021)","journal-title":"IEEE Trans. Image Process"},{"issue":"10","key":"4027_CR2","doi-asserted-by":"publisher","first-page":"6981","DOI":"10.1109\/TCSVT.2022.3178173","volume":"32","author":"G Chen","year":"2022","unstructured":"Chen, G., Liu, S.-J., Sun, Y.-J., Ji, G.-P., Wu, Y.-F., Zhou, T.: Camouflaged object detection via context-aware cross-level fusion. IEEE Trans. Circuits Syst. Video Technol. 32(10), 6981\u20136993 (2022)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"4027_CR3","doi-asserted-by":"crossref","unstructured":"Fan, D.-P., Ji, G.-P., Sun, G., Cheng, M.-M., Shen, J., Shao, L.: Camouflaged object detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 2777\u20132787 (2020)","DOI":"10.1109\/CVPR42600.2020.00285"},{"key":"4027_CR4","doi-asserted-by":"crossref","unstructured":"Mei, H., Ji, G.-P., Wei, Z., Yang, X., Wei, X., Fan, D.-P.: Camouflaged object segmentation with distraction mining. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 8772\u20138781 (2021)","DOI":"10.1109\/CVPR46437.2021.00866"},{"key":"4027_CR5","doi-asserted-by":"crossref","unstructured":"Zhai, Q., Li, X., Yang, F., Chen, C., Cheng, H., Fan, D.-P.: Mutual graph learning for camouflaged object detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 12997\u201313007 (2021)","DOI":"10.1109\/CVPR46437.2021.01280"},{"issue":"1","key":"4027_CR6","doi-asserted-by":"publisher","first-page":"348","DOI":"10.1109\/TIE.1930.896476","volume":"55","author":"A Kumar","year":"2008","unstructured":"Kumar, A.: Computer-vision-based fabric defect detection: a survey. IEEE Trans. Ind. Electron. 55(1), 348\u2013363 (2008)","journal-title":"IEEE Trans. Ind. Electron."},{"key":"4027_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2023.126466","volume":"549","author":"M Liu","year":"2023","unstructured":"Liu, M., Di, X.: Extraordinary mhnet: military high-level camouflage object detection network and dataset. Neurocomputing 549, 126466 (2023)","journal-title":"Neurocomputing"},{"key":"4027_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2024.112051","volume":"299","author":"J Guan","year":"2024","unstructured":"Guan, J., Fang, X., Zhu, T., Qian, W.: Sdrnet: camouflaged object detection with independent reconstruction of structure and detail. Knowl. Based Syst. 299, 112051 (2024)","journal-title":"Knowl. Based Syst."},{"key":"4027_CR9","doi-asserted-by":"crossref","unstructured":"Zhu, J., Zhang, X., Zhang, S., Liu, J.: Inferring camouflaged objects by texture-aware interactive guidance network. In: Proceedings of the AAAI conference on artificial intelligence, vol. 35, pp. 3599\u20133607 (2021)","DOI":"10.1609\/aaai.v35i4.16475"},{"key":"4027_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108414","volume":"123","author":"G-P Ji","year":"2022","unstructured":"Ji, G.-P., Zhu, L., Zhuge, M., Fu, K.: Fast camouflaged object detection via edge-based reversible re-calibration network. Pattern Recognit. 123, 108414 (2022)","journal-title":"Pattern Recognit."},{"key":"4027_CR11","doi-asserted-by":"publisher","first-page":"7036","DOI":"10.1109\/TIP.2022.3217695","volume":"31","author":"T Zhou","year":"2022","unstructured":"Zhou, T., Zhou, Y., Gong, C., Yang, J., Zhang, Y.: Feature aggregation and propagation network for camouflaged object detection. IEEE Trans. Image Process. 31, 7036\u20137047 (2022)","journal-title":"IEEE Trans. Image Process."},{"key":"4027_CR12","doi-asserted-by":"crossref","unstructured":"Zheng, D., Zheng, X., Yang, L.T., Gao, Y., Zhu, C., Ruan, Y.: Mffn: Multi-view feature fusion network for camouflaged object detection. In: Proceedings of the IEEE\/CVF winter conference on applications of computer vision, pp. 6232\u20136242 (2023)","DOI":"10.1109\/WACV56688.2023.00617"},{"key":"4027_CR13","doi-asserted-by":"crossref","unstructured":"Hu, X., Wang, S., Qin, X., Dai, H., Ren, W., Luo, D., Tai, Y., Shao, L.: High-resolution iterative feedback network for camouflaged object detection. In: Proceedings of the AAAI conference on artificial intelligence, vol. 37, pp. 881\u2013889 (2023)","DOI":"10.1609\/aaai.v37i1.25167"},{"key":"4027_CR14","doi-asserted-by":"crossref","unstructured":"Wu, Z., Su, L., Huang, Q.: Stacked cross refinement network for edge-aware salient object detection. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp. 7264\u20137273 (2019)","DOI":"10.1109\/ICCV.2019.00736"},{"issue":"11","key":"4027_CR15","doi-asserted-by":"publisher","first-page":"7772","DOI":"10.1109\/TCSVT.2022.3183641","volume":"32","author":"C Zhang","year":"2022","unstructured":"Zhang, C., Gao, S., Mao, D., Zhou, Y.: Dhnet: Salient object detection with dynamic scale-aware learning and hard-sample refinement. IEEE Trans. Circuits Syst. Video Technol. 32(11), 7772\u20137782 (2022)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"4027_CR16","doi-asserted-by":"publisher","first-page":"1026","DOI":"10.1109\/TIP.2022.3232209","volume":"32","author":"M Ma","year":"2023","unstructured":"Ma, M., Xia, C., Xie, C., Chen, X., Li, J.: Boosting broader receptive fields for salient object detection. IEEE Trans. Image Process. 32, 1026\u20131038 (2023)","journal-title":"IEEE Trans. Image Process."},{"key":"4027_CR17","doi-asserted-by":"publisher","first-page":"4667","DOI":"10.1109\/TMM.2023.3325731","volume":"26","author":"YK Yun","year":"2023","unstructured":"Yun, Y.K., Lin, W.: Towards a complete and detail-preserved salient object detection. IEEE Trans. Multimed. 26, 4667\u20134680 (2023)","journal-title":"IEEE Trans. Multimed."},{"key":"4027_CR18","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2024.3413529","author":"X Zhou","year":"2024","unstructured":"Zhou, X., Shen, K., Liu, Z.: Admnet: Attention-guided densely multi-scale network for lightweight salient object detection. IEEE Transactions on Multimedia (2024). https:\/\/doi.org\/10.1109\/TMM.2024.3413529","journal-title":"IEEE Transactions on Multimedia"},{"key":"4027_CR19","doi-asserted-by":"crossref","unstructured":"Feng, X., Guoying, C., Wei, S.: Camouflage texture evaluation using saliency map. In: Proceedings of the fifth international conference on internet multimedia computing and service, pp. 93\u201396 (2013)","DOI":"10.1145\/2499788.2499877"},{"issue":"9","key":"4027_CR20","doi-asserted-by":"publisher","first-page":"2001","DOI":"10.1109\/TCSVT.2016.2555719","volume":"27","author":"X Zhang","year":"2016","unstructured":"Zhang, X., Zhu, C., Wang, S., Liu, Y., Ye, M.: A bayesian approach to camouflaged moving object detection. IEEE Trans. Circuits Syst. Video Technol. 27(9), 2001\u20132013 (2016)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"issue":"8","key":"4027_CR21","doi-asserted-by":"publisher","first-page":"3918","DOI":"10.1109\/TIP.2018.2828329","volume":"27","author":"S Li","year":"2018","unstructured":"Li, S., Florencio, D., Li, W., Zhao, Y., Cook, C.: A fusion framework for camouflaged moving foreground detection in the wavelet domain. IEEE Trans. Image Process. 27(8), 3918\u20133930 (2018)","journal-title":"IEEE Trans. Image Process."},{"key":"4027_CR22","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/j.cviu.2019.04.006","volume":"184","author":"T-N Le","year":"2019","unstructured":"Le, T.-N., Nguyen, T.V., Nie, Z., Tran, M.-T., Sugimoto, A.: Anabranch network for camouflaged object segmentation. Comput. Vis. Image Underst. 184, 45\u201356 (2019)","journal-title":"Comput. Vis. Image Underst."},{"issue":"6","key":"4027_CR23","first-page":"7","volume":"2","author":"P Skurowski","year":"2018","unstructured":"Skurowski, P., Abdulameer, H., B\u0142aszczyk, J., Depta, T., Kornacki, A., Kozie\u0142, P.: Animal camouflage analysis: Chameleon database. Unpublished manuscript 2(6), 7 (2018)","journal-title":"Unpublished manuscript"},{"key":"4027_CR24","doi-asserted-by":"crossref","unstructured":"Pang, Y., Zhao, X., Xiang, T.-Z., Zhang, L., Lu, H.: Zoom in and out: a mixed-scale triplet network for camouflaged object detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 2160\u20132170 (2022)","DOI":"10.1109\/CVPR52688.2022.00220"},{"issue":"3","key":"4027_CR25","doi-asserted-by":"publisher","first-page":"1157","DOI":"10.1109\/TCSVT.2021.3126591","volume":"33","author":"J Ren","year":"2021","unstructured":"Ren, J., Hu, X., Zhu, L., Xu, X., Xu, Y., Wang, W., Deng, Z., Heng, P.-A.: Deep texture-aware features for camouflaged object detection. IEEE Trans. Circuits Syst. Video Technol. 33(3), 1157\u20131167 (2021)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"issue":"9","key":"4027_CR26","doi-asserted-by":"publisher","first-page":"4934","DOI":"10.1109\/TCSVT.2023.3245883","volume":"33","author":"Y Liu","year":"2023","unstructured":"Liu, Y., Li, H., Cheng, J., Chen, X.: Mscaf-net: a general framework for camouflaged object detection via learning multi-scale context-aware features. IEEE Trans. Circuits Syst. Video Technol. 33(9), 4934\u20134947 (2023)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"issue":"7","key":"4027_CR27","doi-asserted-by":"publisher","first-page":"5452","DOI":"10.1109\/TCSVT.2023.3349209","volume":"34","author":"X Hu","year":"2024","unstructured":"Hu, X., Zhang, X., Wang, F., Sun, J., Sun, F.: Efficient camouflaged object detection network based on global localization perception and local guidance refinement. IEEE Trans. Circuits Syst. Video Technol. 34(7), 5452\u20135465 (2024)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"4027_CR28","doi-asserted-by":"crossref","unstructured":"Wang, L., Yang, J., Zhang, Y., Wang, F., Zheng, F.: Depth-aware concealed crop detection in dense agricultural scenes. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 17201\u201317211 (2024)","DOI":"10.1109\/CVPR52733.2024.01628"},{"issue":"1","key":"4027_CR29","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1007\/s11633-022-1365-9","volume":"20","author":"G-P Ji","year":"2023","unstructured":"Ji, G.-P., Fan, D.-P., Chou, Y.-C., Dai, D., Liniger, A., Van Gool, L.: Deep gradient learning for efficient camouflaged object detection. Mach. Intell. Res. 20(1), 92\u2013108 (2023)","journal-title":"Mach. Intell. Res."},{"issue":"3","key":"4027_CR30","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1007\/s41095-022-0274-8","volume":"8","author":"W Wang","year":"2022","unstructured":"Wang, W., Xie, E., Li, X., Fan, D.-P., Song, K., Liang, D., Lu, T., Luo, P., Shao, L.: Pvt v2: Improved baselines with pyramid vision transformer. Comput. Vis. Media 8(3), 415\u2013424 (2022)","journal-title":"Comput. Vis. Media"},{"key":"4027_CR31","first-page":"1","volume":"20","author":"J Jin","year":"2023","unstructured":"Jin, J., Zhou, W., Yang, R., Ye, L., Yu, L.: Edge detection guide network for semantic segmentation of remote-sensing images. IEEE Geosci. Remote. Sens. Lett. 20, 1\u20135 (2023)","journal-title":"IEEE Geosci. Remote. Sens. Lett."},{"key":"4027_CR32","doi-asserted-by":"crossref","unstructured":"Chen, Y., Fan, H., Xu, B., Yan, Z., Kalantidis, Y., Rohrbach, M., Yan, S., Feng, J.: Drop an octave: reducing spatial redundancy in convolutional neural networks with octave convolution. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp. 3435\u20133444 (2019)","DOI":"10.1109\/ICCV.2019.00353"},{"key":"4027_CR33","doi-asserted-by":"crossref","unstructured":"Sun, Y., Chen, G., Zhou, T., Zhang, Y., Liu, N.: Context-aware cross-level fusion network for camouflaged object detection. arXiv preprint arXiv:2105.12555 (2021)","DOI":"10.24963\/ijcai.2021\/142"},{"key":"4027_CR34","doi-asserted-by":"publisher","DOI":"10.1007\/s00371-024-03688-6","author":"Y Ge","year":"2024","unstructured":"Ge, Y., Ren, J., Zhang, C., He, M., Bi, H., Zhang, Q.: Feature-aware and iterative refinement network for camouflaged object detection. The Visual Computer (2024). https:\/\/doi.org\/10.1007\/s00371-024-03688-6","journal-title":"The Visual Computer"},{"key":"4027_CR35","doi-asserted-by":"crossref","unstructured":"Fan, D.-P., Cheng, M.-M., Liu, Y., Li, T., Borji, A.: Structure-measure: a new way to evaluate foreground maps. In: Proceedings of the IEEE international conference on computer vision, pp. 4548\u20134557 (2017)","DOI":"10.1109\/ICCV.2017.487"},{"key":"4027_CR36","doi-asserted-by":"crossref","unstructured":"Fan, D.-P., Gong, C., Cao, Y., Ren, B., Cheng, M.-M., Borji, A.: Enhanced-alignment measure for binary foreground map evaluation. arXiv preprint arXiv:1805.10421 (2018)","DOI":"10.24963\/ijcai.2018\/97"},{"key":"4027_CR37","doi-asserted-by":"crossref","unstructured":"Margolin, R., Zelnik-Manor, L., Tal, A.: How to evaluate foreground maps? In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 248\u2013255 (2014)","DOI":"10.1109\/CVPR.2014.39"},{"key":"4027_CR38","doi-asserted-by":"crossref","unstructured":"Qin, X., Zhang, Z., Huang, C., Gao, C., Dehghan, M., Jagersand, M.: Basnet: Boundary-aware salient object detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 7479\u20137489 (2019)","DOI":"10.1109\/CVPR.2019.00766"},{"key":"4027_CR39","doi-asserted-by":"crossref","unstructured":"Zhao, J.-X., Liu, J.-J., Fan, D.-P., Cao, Y., Yang, J., Cheng, M.-M.: Egnet: Edge guidance network for salient object detection. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp. 8779\u20138788 (2019)","DOI":"10.1109\/ICCV.2019.00887"},{"key":"4027_CR40","doi-asserted-by":"crossref","unstructured":"Fan, D.-P., Ji, G.-P., Zhou, T., Chen, G., Fu, H., Shen, J., Shao, L.: Pranet: Parallel reverse attention network for polyp segmentation. In: International conference on medical image computing and computer-assisted intervention, pp. 263\u2013273 (2020). Springer","DOI":"10.1007\/978-3-030-59725-2_26"},{"issue":"3","key":"4027_CR41","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3570507","volume":"19","author":"S Hui","year":"2023","unstructured":"Hui, S., Guo, Q., Geng, X., Zhang, C.: Multi-guidance cnns for salient object detection. ACM Trans. Multimed. Comput., Commun. Appl. 19(3), 1\u201319 (2023)","journal-title":"ACM Trans. Multimed. Comput., Commun. Appl."},{"key":"4027_CR42","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2025.127056","volume":"275","author":"B Liang","year":"2025","unstructured":"Liang, B., Luo, H., Wang, J., Shark, L.-K.: Multi-scale attention-edge interactive refinement network for salient object detection. Expert. Syst. Appl. 275, 127056 (2025)","journal-title":"Expert. Syst. Appl."},{"issue":"10","key":"4027_CR43","doi-asserted-by":"publisher","first-page":"6024","DOI":"10.1109\/TPAMI.2021.3085766","volume":"44","author":"D-P Fan","year":"2021","unstructured":"Fan, D.-P., Ji, G.-P., Cheng, M.-M., Shao, L.: Concealed object detection. IEEE Trans. Pattern Anal. Mach. Intell. 44(10), 6024\u20136042 (2021)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"4027_CR44","doi-asserted-by":"crossref","unstructured":"Sun, Y., Wang, S., Chen, C., Xiang, T.-Z.: Boundary-guided camouflaged object detection. arXiv preprint arXiv:2207.00794 (2022)","DOI":"10.24963\/ijcai.2022\/186"},{"key":"4027_CR45","doi-asserted-by":"crossref","unstructured":"He, C., Li, K., Zhang, Y., Tang, L., Zhang, Y., Guo, Z., Li, X.: Camouflaged object detection with feature decomposition and edge reconstruction. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 22046\u201322055 (2023)","DOI":"10.1109\/CVPR52729.2023.02111"},{"issue":"10","key":"4027_CR46","doi-asserted-by":"publisher","first-page":"4593","DOI":"10.1007\/s00371-022-02611-1","volume":"39","author":"Q Zhang","year":"2023","unstructured":"Zhang, Q., Ge, Y., Zhang, C., Bi, H.: Tprnet: camouflaged object detection via transformer-induced progressive refinement network. Vis. Comput. 39(10), 4593\u20134607 (2023)","journal-title":"Vis. Comput."},{"key":"4027_CR47","doi-asserted-by":"crossref","unstructured":"Yang, F., Zhai, Q., Li, X., Huang, R., Luo, A., Cheng, H., Fan, D.-P.: Uncertainty-guided transformer reasoning for camouflaged object detection. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp. 4146\u20134155 (2021)","DOI":"10.1109\/ICCV48922.2021.00411"},{"key":"4027_CR48","doi-asserted-by":"publisher","first-page":"7114","DOI":"10.1109\/TMM.2024.3360710","volume":"26","author":"X Zhou","year":"2024","unstructured":"Zhou, X., Wu, Z., Cong, R.: Decoupling and integration network for camouflaged object detection. IEEE Trans. Multimed. 26, 7114\u20137129 (2024)","journal-title":"IEEE Trans. Multimed."},{"issue":"15","key":"4027_CR49","doi-asserted-by":"publisher","first-page":"7531","DOI":"10.1007\/s10489-024-05559-y","volume":"54","author":"Z Tang","year":"2024","unstructured":"Tang, Z., Tang, J., Zou, D., Rao, J., Qi, F.: Two guidance joint network based on coarse map and edge map for camouflaged object detection. Appl. Intell. 54(15), 7531\u20137544 (2024)","journal-title":"Appl. Intell."},{"key":"4027_CR50","doi-asserted-by":"crossref","unstructured":"Huang, Z., Dai, H., Xiang, T.-Z., Wang, S., Chen, H.-X., Qin, J., Xiong, H.: Feature shrinkage pyramid for camouflaged object detection with transformers. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5557\u20135566 (2023)","DOI":"10.1109\/CVPR52729.2023.00538"},{"key":"4027_CR51","doi-asserted-by":"crossref","unstructured":"Ye, Q., Li, Q., Huo, G., Liu, Y., Zhou, Y.: Boundary-guided multi-scale refinement network for camouflaged object detection. The Visual Computer, 1\u201327 (2025)","DOI":"10.1007\/s00371-024-03786-5"},{"key":"4027_CR52","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"issue":"2","key":"4027_CR53","doi-asserted-by":"publisher","first-page":"652","DOI":"10.1109\/TPAMI.2019.2938758","volume":"43","author":"S-H Gao","year":"2019","unstructured":"Gao, S.-H., Cheng, M.-M., Zhao, K., Zhang, X.-Y., Yang, M.-H., Torr, P.: Res2net: A new multi-scale backbone architecture. IEEE Trans. Pattern Anal. Mach. Intell. 43(2), 652\u2013662 (2019)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"4027_CR54","unstructured":"Tan, M., Le, Q.: Efficientnet: Rethinking model scaling for convolutional neural networks. In: international conference on machine learning, pp. 6105\u20136114 (2019). PMLR"},{"key":"4027_CR55","doi-asserted-by":"crossref","unstructured":"Jha, D., Smedsrud, P.H., Riegler, M.A., Halvorsen, P., De\u00a0Lange, T., Johansen, D., Johansen, H.D.: Kvasir-seg: a segmented polyp dataset. In: International conference on multimedia modeling, pp. 451\u2013462 (2019). Springer","DOI":"10.1007\/978-3-030-37734-2_37"},{"key":"4027_CR56","doi-asserted-by":"crossref","unstructured":"Bernal, J., S\u00e1nchez, F.J., Fern\u00e1ndez-Esparrach, G., Gil, D., Rodr\u00edguez, C., Vilari\u00f1o, F.: Wm-dova maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians. Computerized medical imaging and graphics 43, 99\u2013111 (2015)","DOI":"10.1016\/j.compmedimag.2015.02.007"},{"key":"4027_CR57","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1007\/s11548-013-0926-3","volume":"9","author":"J Silva","year":"2014","unstructured":"Silva, J., Histace, A., Romain, O., Dray, X., Granado, B.: Toward embedded detection of polyps in wce images for early diagnosis of colorectal cancer. Int. J. Comput. Assist. Radiol. Surg. 9, 283\u2013293 (2014)","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"issue":"2","key":"4027_CR58","doi-asserted-by":"publisher","first-page":"630","DOI":"10.1109\/TMI.2015.2487997","volume":"35","author":"N Tajbakhsh","year":"2015","unstructured":"Tajbakhsh, N., Gurudu, S.R., Liang, J.: Automated polyp detection in colonoscopy videos using shape and context information. IEEE Trans. Med. Imaging 35(2), 630\u2013644 (2015)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"4027_CR59","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-assisted intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18, pp. 234\u2013241 (2015). Springer","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"4027_CR60","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Rahman\u00a0Siddiquee, M.M., Tajbakhsh, N., Liang, J.: Unet++: a nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support: 4th international workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings 4, pp. 3\u201311 (2018). Springer","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"4027_CR61","doi-asserted-by":"crossref","unstructured":"Fang, Y., Chen, C., Yuan, Y., Tong, K.-y.: Selective feature aggregation network with area-boundary constraints for polyp segmentation. In: Medical image computing and computer assisted intervention\u2013MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13\u201317, 2019, Proceedings, Part I 22, pp. 302\u2013310 (2019). Springer","DOI":"10.1007\/978-3-030-32239-7_34"}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-025-04027-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-025-04027-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-025-04027-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,15]],"date-time":"2025-09-15T09:35:16Z","timestamp":1757928916000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-025-04027-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,10]]},"references-count":61,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2025,9]]}},"alternative-id":["4027"],"URL":"https:\/\/doi.org\/10.1007\/s00371-025-04027-z","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"type":"print","value":"0178-2789"},{"type":"electronic","value":"1432-2315"}],"subject":[],"published":{"date-parts":[[2025,6,10]]},"assertion":[{"value":"19 May 2025","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 June 2025","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no conflict of interest to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}