{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T00:13:59Z","timestamp":1793319239662,"version":"4.5.0"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T00:00:00Z","timestamp":1767744000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T00:00:00Z","timestamp":1769731200000},"content-version":"vor","delay-in-days":23,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-025-34548-5","type":"journal-article","created":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T03:03:56Z","timestamp":1767755036000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Enhanced colorectal gland segmentation through multi-scale attention and contextual feature fusion"],"prefix":"10.1038","volume":"16","author":[{"given":"Ponnarasee","family":"B. K.","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lalithamani","family":"N.","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adeyemi Abel","family":"Ajibesin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,1,7]]},"reference":[{"key":"34548_CR1","first-page":"142","volume":"44","author":"S Shivshankar","year":"2025","unstructured":"Shivshankar, S., Patil, P. S., Deodhar, K. & Budukh, A. M. Epidemiology of colorectal cancer: A review with special emphasis on india. Indian J. Gastroenterol. 44, 142\u2013153 (2025).","journal-title":"Indian J. Gastroenterol."},{"key":"34548_CR2","doi-asserted-by":"crossref","unstructured":"Vishakan, A. S. et al. Decadal trends and associated factors of colorectal cancer in kerala between 2010 and 2021 among patients in a tertiary hospital-based cancer registry. Cureus 16, e75030 (2024).","DOI":"10.7759\/cureus.75030"},{"key":"34548_CR3","doi-asserted-by":"publisher","first-page":"45","DOI":"10.25259\/Cytojournal_272_2024","volume":"22","author":"J Wang","year":"2025","unstructured":"Wang, J., Wang, T., Han, R., Shi, D. & Chen, B. Artificial intelligence in cancer pathology: Applications, challenges, and future directions. CytoJournal 22, 45 (2025).","journal-title":"CytoJournal"},{"key":"34548_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpi.2023.100319","volume":"14","author":"S Krishna","year":"2023","unstructured":"Krishna, S., Suganthi, S., Bhavsar, A., Yesodharan, J. & Krishnamoorthy, S. An interpretable decision-support model for breast cancer diagnosis using histopathology images. J. Pathol. Inform. 14, 100319 (2023).","journal-title":"J. Pathol. Inform."},{"key":"34548_CR5","doi-asserted-by":"publisher","first-page":"564","DOI":"10.1002\/path.6168","volume":"260","author":"A Asif","year":"2023","unstructured":"Asif, A. et al. Unleashing the potential of ai for pathology: challenges and recommendations. J. Pathol. Inform. 260, 564\u2013577 (2023).","journal-title":"J. Pathol. Inform."},{"key":"34548_CR6","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1186\/s13000-023-01375-z","volume":"18","author":"S Shafi","year":"2023","unstructured":"Shafi, S. & Parwani, A. V. Artificial intelligence in diagnostic pathology. Diagn. Pathol. 18, 109 (2023).","journal-title":"Diagn. Pathol."},{"key":"34548_CR7","doi-asserted-by":"publisher","first-page":"930","DOI":"10.1038\/s44222-023-00096-8","volume":"1","author":"AH Song","year":"2023","unstructured":"Song, A. H. et al. Artificial intelligence for digital and computational pathology. Nat. Rev. Bioeng. 1, 930\u2013949 (2023).","journal-title":"Nat. Rev. Bioeng."},{"key":"34548_CR8","doi-asserted-by":"crossref","unstructured":"Berb\u00eds, M. A. et al. Computational pathology in 2030: a delphi study forecasting the role of ai in pathology within the next decade. EBioMedicine 88, 104427 (2023).","DOI":"10.1016\/j.ebiom.2022.104427"},{"key":"34548_CR9","doi-asserted-by":"publisher","first-page":"5383","DOI":"10.1007\/s00521-021-06687-z","volume":"34","author":"P Rastogi","year":"2022","unstructured":"Rastogi, P., Khanna, K. & Singh, V. Gland segmentation in colorectal cancer histopathological images using u-net inspired convolutional network. Neural Comput. Appl. 34, 5383\u20135395 (2022).","journal-title":"Neural Comput. Appl."},{"key":"34548_CR10","doi-asserted-by":"publisher","first-page":"1199","DOI":"10.3390\/cancers14051199","volume":"14","author":"Y Wu","year":"2022","unstructured":"Wu, Y. et al. Recent advances of deep learning for computational histopathology: principles and applications. Cancers 14, 1199 (2022).","journal-title":"Cancers"},{"key":"34548_CR11","doi-asserted-by":"crossref","unstructured":"Ponnarasee, B. et al. Stainsegnet: Stain normalization and segmentation-driven colorectal tumor tissues classification. In: 2023 International Conference on Computational Science and Computational Intelligence (CSCI), 1202\u20131207 (2023).","DOI":"10.1109\/CSCI62032.2023.00196"},{"key":"34548_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.104699","volume":"136","author":"H Zunair","year":"2021","unstructured":"Zunair, H. & Hamza, A. B. Sharp u-net: Depthwise convolutional network for biomedical image segmentation. Comput. Biol. Med. 136, 104699 (2021).","journal-title":"Comput. Biol. Med."},{"key":"34548_CR13","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Rahman Siddiquee, M.\u00a0M., Tajbakhsh, N. & Liang, J. Unet++: A nested u-net architecture for medical image segmentation. In International workshop on deep learning in medical image analysis, 3\u201311 (2018).","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"34548_CR14","doi-asserted-by":"publisher","first-page":"3907","DOI":"10.1109\/TMI.2023.3317132","volume":"42","author":"Y Li","year":"2023","unstructured":"Li, Y. et al. A hierarchical graph v-net with semi-supervised pre-training for histological image based breast cancer classification. IEEE Trans. Med. Imaging 42, 3907\u20133918 (2023).","journal-title":"IEEE Trans. Med. Imaging"},{"key":"34548_CR15","doi-asserted-by":"publisher","first-page":"8589","DOI":"10.3390\/s23208589","volume":"23","author":"A Al Qurri","year":"2023","unstructured":"Al Qurri, A. & Almekkawy, M. Improved unet with attention for medical image segmentation. Sensors 23, 8589 (2023).","journal-title":"Sensors"},{"key":"34548_CR16","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-cs.2444","volume":"10","author":"SM Shafi","year":"2024","unstructured":"Shafi, S. M. & Chinnappan, S. K. Hybrid transformer-cnn and lstm model for lung disease segmentation and classification. PeerJ Comput. Sci. 10, e2444 (2024).","journal-title":"PeerJ Comput. Sci."},{"key":"34548_CR17","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1186\/s12880-023-00964-0","volume":"23","author":"MM Srikantamurthy","year":"2023","unstructured":"Srikantamurthy, M. M., Rallabandi, V. S., Dudekula, D. B., Natarajan, S. & Park, J. Classification of benign and malignant subtypes of breast cancer histopathology imaging using hybrid cnn-lstm based transfer learning. BMC Med. Imaging 23, 19 (2023).","journal-title":"BMC Med. Imaging"},{"key":"34548_CR18","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y. et al. Feature pyramid networks for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2117\u20132125 (2017).","DOI":"10.1109\/CVPR.2017.106"},{"key":"34548_CR19","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L. & Sun, G. Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 7132\u20137141 (2018).","DOI":"10.1109\/CVPR.2018.00745"},{"key":"34548_CR20","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X. & Jia, J. Pyramid scene parsing network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2881\u20132890 (2017).","DOI":"10.1109\/CVPR.2017.660"},{"key":"34548_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106365","volume":"152","author":"X Li","year":"2023","unstructured":"Li, X. et al. Attransunet: An enhanced hybrid transformer architecture for ultrasound and histopathology image segmentation. Comput. Biol. Med. 152, 106365 (2023).","journal-title":"Comput. Biol. Med."},{"key":"34548_CR22","volume":"266","author":"S Sengupta","year":"2025","unstructured":"Sengupta, S. et al. Assessment of different u-net backbones in segmenting colorectal adenocarcinoma from h&e histopathology. Comput. Biol. Med. 266, 155820 (2025).","journal-title":"Comput. Biol. Med."},{"key":"34548_CR23","doi-asserted-by":"publisher","first-page":"1114673","DOI":"10.3389\/fmed.2023.1114673","volume":"10","author":"L Shi","year":"2023","unstructured":"Shi, L. et al. Ebhi-seg: A novel enteroscope biopsy histopathological hematoxylin and eosin image dataset for image segmentation tasks. Front. Med. 10, 1114673 (2023).","journal-title":"Front. Med."},{"key":"34548_CR24","doi-asserted-by":"publisher","unstructured":"Cheng, H. et al. Glandular tissue segmentation based on ema-swin unet model. Authorea Preprint, https:\/\/doi.org\/10.22541\/au.173641600.01007870\/v1 (2025).","DOI":"10.22541\/au.173641600.01007870\/v1"},{"key":"34548_CR25","doi-asserted-by":"crossref","unstructured":"Zhu, J. et al. Dmoc-unet: A dynamic mixture-of-convolution network for enhanced pathological image segmentation. IEEE Access 13, 82652\u201382667 (2025).","DOI":"10.1109\/ACCESS.2025.3568027"},{"key":"34548_CR26","doi-asserted-by":"crossref","unstructured":"Nguyen, C., Asad, Z., Deng, R. & Huo, Y. Evaluating transformer-based semantic segmentation networks for pathological image segmentation. In: Medical Imaging 2022: Image Processing, vol. 12032, 942\u2013947 (SPIE, 2022).","DOI":"10.1117\/12.2611177"},{"key":"34548_CR27","doi-asserted-by":"publisher","first-page":"68697","DOI":"10.1109\/ACCESS.2024.3397667","volume":"12","author":"V Sreelekshmi","year":"2024","unstructured":"Sreelekshmi, V., Pavithran, K. & Nair, J. J. Swincnn: an integrated swin transformer and cnn for improved breast cancer grade classification. IEEE Access 12, 68697\u201368710 (2024).","journal-title":"IEEE Access"},{"key":"34548_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2023.107718","volume":"240","author":"A Khan","year":"2023","unstructured":"Khan, A., Han, S., Ilyas, N., Lee, Y.-M. & Lee, B. Cervixformer: A multi-scale swin transformer-based cervical pap-smear wsi classification framework. Comput. Methods Programs Biomed. 240, 107718 (2023).","journal-title":"Comput. Methods Programs Biomed."},{"key":"34548_CR29","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1016\/j.media.2018.12.001","volume":"52","author":"S Graham","year":"2019","unstructured":"Graham, S. et al. Mild-net: Minimal information loss dilated network for gland instance segmentation in colon histology images. Med. Image Anal. 52, 199\u2013211 (2019).","journal-title":"Med. Image Anal."},{"key":"34548_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2024.110621","volume":"155","author":"H Wang","year":"2024","unstructured":"Wang, H., Ahn, E. & Kim, J. A multi-resolution self-supervised learning framework for semantic segmentation in histopathology. Pattern Recognit. 155, 110621 (2024).","journal-title":"Pattern Recognit."},{"key":"34548_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102685","volume":"83","author":"S Graham","year":"2023","unstructured":"Graham, S. et al. One model is all you need: multi-task learning enables simultaneous histology image segmentation and classification. Med. Image Anal. 83, 102685 (2023).","journal-title":"Med. Image Anal."},{"key":"34548_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2025.105419","volume":"154","author":"M Raveenthini","year":"2025","unstructured":"Raveenthini, M., Lavanya, R. & Benitez, R. Grad-cam based explanations for multiocular disease detection using xception net. Image Vis. Comput. 154, 105419 (2025).","journal-title":"Image Vis. Comput."},{"key":"34548_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2023.110003","volume":"146","author":"SH Yelleni","year":"2024","unstructured":"Yelleni, S. H. et al. Monte carlo dropblock for modeling uncertainty in object detection. Pattern Recognit. 146, 110003 (2024).","journal-title":"Pattern Recognit."},{"key":"34548_CR34","doi-asserted-by":"publisher","first-page":"6506","DOI":"10.1038\/s41598-025-90221-x","volume":"15","author":"G Sudhamsh","year":"2025","unstructured":"Sudhamsh, G., Girisha, S. & Rashmi, R. Semi-supervised tissue segmentation from histopathological images with consistency regularization and uncertainty estimation. Sci. Rep. 15, 6506 (2025).","journal-title":"Sci. Rep."},{"key":"34548_CR35","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1016\/j.media.2016.08.008","volume":"35","author":"K Sirinukunwattana","year":"2017","unstructured":"Sirinukunwattana, K. et al. Gland segmentation in colon histology images: The glas challenge contest. Med. Image Anal. 35, 489\u2013502 (2017).","journal-title":"Med. Image Anal."},{"key":"34548_CR36","doi-asserted-by":"publisher","first-page":"5689","DOI":"10.1007\/s00371-024-03746-z","volume":"41","author":"X Zhang","year":"2025","unstructured":"Zhang, X. et al. Hadiff: hierarchy aggregated diffusion model for pathology image segmentation. The Visual Computer 41, 5689\u20135700 (2025).","journal-title":"The Visual Computer"},{"key":"34548_CR37","unstructured":"Gamper, J. et al. Pannuke dataset extension, insights and baselines. arXiv preprint arXiv:2003.10778 (2020)."},{"key":"34548_CR38","doi-asserted-by":"publisher","first-page":"3461","DOI":"10.1093\/bioinformatics\/btz083","volume":"35","author":"M Amgad","year":"2019","unstructured":"Amgad, M. et al. Structured crowdsourcing enables convolutional segmentation of histology images. Bioinformatics 35, 3461\u20133467 (2019).","journal-title":"Bioinformatics"},{"key":"34548_CR39","doi-asserted-by":"publisher","first-page":"3446","DOI":"10.1109\/TMI.2021.3087857","volume":"40","author":"R Liu","year":"2021","unstructured":"Liu, R. et al. Nhbs-net: A feature fusion attention network for ultrasound neonatal hip bone segmentation. IEEE Trans. Med. Imaging 40, 3446\u20133458 (2021).","journal-title":"IEEE Trans. Med. Imaging"},{"key":"34548_CR40","doi-asserted-by":"publisher","first-page":"5404","DOI":"10.1038\/s41598-025-86428-7","volume":"15","author":"J Juybari","year":"2025","unstructured":"Juybari, J., Hamilton, J., Chen, C., Khalil, A. & Zhu, Y. Context-guided segmentation for histopathologic cancer segmentation. Sci. Rep. 15, 5404 (2025).","journal-title":"Sci. Rep."},{"key":"34548_CR41","first-page":"6953","volume":"38","author":"M Zha","year":"2024","unstructured":"Zha, M. et al. Weakly-supervised mirror detection via scribble annotations. Proc. AAAI Conf. Artif. Intell. 38, 6953\u20136961 (2024).","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"34548_CR42","doi-asserted-by":"crossref","unstructured":"Zha, M. et al. Heterogeneous experts and hierarchical perception for underwater salient object detection. IEEE Trans. Image Process. 34, 3703\u20133717 (2025).","DOI":"10.1109\/TIP.2025.3572760"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-34548-5","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-34548-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-34548-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T10:23:56Z","timestamp":1769768636000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-34548-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,7]]},"references-count":42,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["34548"],"URL":"https:\/\/doi.org\/10.1038\/s41598-025-34548-5","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,7]]},"assertion":[{"value":"11 October 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 December 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 January 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"During the preparation of this work the authors used ChatGPT and Grammarly minimally for language refinement only. After using this tool\/service, the authors reviewed and edited the content as needed and take(s) full responsibility for the content of the publication.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declaration of generative AI and AI-assisted technologies in the writing process"}}],"article-number":"4300"}}