{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T20:57:02Z","timestamp":1757624222570,"version":"3.44.0"},"reference-count":27,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T00:00:00Z","timestamp":1757376000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T00:00:00Z","timestamp":1757376000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Intell Syst"],"DOI":"10.1007\/s44196-025-00965-7","type":"journal-article","created":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T11:02:33Z","timestamp":1757415753000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Smart Diagnosis of Cholangiocarcinoma from Microscopic Images Using a Modified Visual Geometry Group Network with Adaptive Augmentation"],"prefix":"10.1007","volume":"18","author":[{"given":"Muhammad","family":"Mujahid","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Khadija","family":"Kanwal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Muhammad","family":"Abubakar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shaha","family":"Al-Otaibi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alex","family":"Elyassih","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amjad","family":"Rehman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,9]]},"reference":[{"issue":"1","key":"965_CR1","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1038\/s41572-021-00300-2","volume":"7","author":"PJ Brindley","year":"2021","unstructured":"Brindley, P.J., Bachini, M., Ilyas, S.I., Khan, S.A., Loukas, A., Sirica, A.E., Teh, B.T., Wongkham, S., Gores, G.J.: Cholangiocarcinoma. Nat. Rev. Disease Prime. 7(1), 65 (2021)","journal-title":"Nat. Rev. Disease Prime."},{"issue":"6","key":"965_CR2","doi-asserted-by":"publisher","first-page":"1690","DOI":"10.1016\/j.jhep.2022.07.022","volume":"77","author":"M Vithayathil","year":"2022","unstructured":"Vithayathil, M., Khan, S.A.: Current epidemiology of cholangiocarcinoma in western countries. J. Hepatol. 77(6), 1690\u20131698 (2022)","journal-title":"J. Hepatol."},{"issue":"6","key":"965_CR3","doi-asserted-by":"publisher","first-page":"856","DOI":"10.1016\/j.jhep.2006.09.001","volume":"45","author":"H Malhi","year":"2006","unstructured":"Malhi, H., Gores, G.J.: Cholangiocarcinoma: modern advances in understanding a deadly old disease. J. Hepatol. 45(6), 856\u2013867 (2006)","journal-title":"J. Hepatol."},{"key":"965_CR4","unstructured":"Malka, D., Siebenh\u00fcner, A.R., Mertens, J.C., Schirmacher, P.: The importance of molecular testing in the treatment of cholangiocarcinoma. Oncology (2020)"},{"key":"965_CR5","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1111\/liv.14094","volume":"39","author":"S Vicent","year":"2019","unstructured":"Vicent, S., Lieshout, R., Saborowski, A., Verstegen, M.M., Raggi, C., Recalcati, S., Invernizzi, P., Laan, L.J., Alvaro, D., Calvisi, D.F., et al.: Experimental models to unravel the molecular pathogenesis, cell of origin and stem cell properties of cholangiocarcinoma. Liver Int. 39, 79\u201397 (2019)","journal-title":"Liver Int."},{"issue":"1","key":"965_CR6","doi-asserted-by":"publisher","first-page":"5639","DOI":"10.1038\/s41467-021-25296-x","volume":"12","author":"S Cheng","year":"2021","unstructured":"Cheng, S., Liu, S., Yu, J., Rao, G., Xiao, Y., Han, W., Zhu, W., Lv, X., Li, N., Cai, J., et al.: Robust whole slide image analysis for cervical cancer screening using deep learning. Nat. Commun. 12(1), 5639 (2021)","journal-title":"Nat. Commun."},{"issue":"24","key":"965_CR7","doi-asserted-by":"publisher","first-page":"9790","DOI":"10.3390\/s22249790","volume":"22","author":"R Cui","year":"2022","unstructured":"Cui, R., Yu, H., Xu, T., Xing, X., Cao, X., Yan, K., Chen, J.: Deep learning in medical hyperspectral images: a review. Sensors 22(24), 9790 (2022)","journal-title":"Sensors"},{"issue":"1","key":"965_CR8","doi-asserted-by":"publisher","first-page":"18854","DOI":"10.1038\/s41598-023-46152-6","volume":"13","author":"S Chakrabarti","year":"2023","unstructured":"Chakrabarti, S., Rao, U.S.: Lightweight neural network for smart diagnosis of cholangiocarcinoma using histopathological images. Sci. Rep. 13(1), 18854 (2023)","journal-title":"Sci. Rep."},{"issue":"1","key":"965_CR9","doi-asserted-by":"publisher","first-page":"7924","DOI":"10.1038\/s41598-022-11997-w","volume":"12","author":"R Hu","year":"2022","unstructured":"Hu, R., Li, H., Horng, H., Thomasian, N.M., Jiao, Z., Zhu, C., Zou, B., Bai, H.X.: Automated machine learning for differentiation of hepatocellular carcinoma from intrahepatic cholangiocarcinoma on multiphasic mri. Sci. Rep. 12(1), 7924 (2022)","journal-title":"Sci. Rep."},{"issue":"7","key":"965_CR10","doi-asserted-by":"publisher","first-page":"1948","DOI":"10.1053\/j.gastro.2022.02.025","volume":"162","author":"N Cheng","year":"2022","unstructured":"Cheng, N., Ren, Y., Zhou, J., Zhang, Y., Wang, D., Zhang, X., Chen, B., Liu, F., Lv, J., Cao, Q., et al.: Deep learning-based classification of hepatocellular nodular lesions on whole-slide histopathologic images. Gastroenterology 162(7), 1948\u20131961 (2022)","journal-title":"Gastroenterology"},{"key":"965_CR11","doi-asserted-by":"publisher","first-page":"149414","DOI":"10.1109\/ACCESS.2019.2947470","volume":"7","author":"Q Zhang","year":"2019","unstructured":"Zhang, Q., Li, Q., Yu, G., Sun, L., Zhou, M., Chu, J.: A multidimensional choledoch database and benchmarks for cholangiocarcinoma diagnosis. IEEE Access 7, 149414\u2013149421 (2019). https:\/\/doi.org\/10.1109\/ACCESS.2019.2947470","journal-title":"IEEE Access"},{"key":"965_CR12","doi-asserted-by":"publisher","unstructured":"Xiao, H., Wang, J., Weng, Z., Lin, X., Shu, M., Shen, J., Sun, P., Cai, M., Xiang, X., Li, B., et al.: A histopathology-based artificial intelligence system assisting the screening of genetic alteration in intrahepatic cholangiocarcinoma. Br. J. Cancer 1\u20138 (2024). https:\/\/doi.org\/10.1038\/s41416-024-02910-5","DOI":"10.1038\/s41416-024-02910-5"},{"issue":"8","key":"965_CR13","doi-asserted-by":"publisher","first-page":"1311","DOI":"10.1364\/OPTCON.527576","volume":"3","author":"SS Kumar","year":"2024","unstructured":"Kumar, S.S., Sahoo, O.P., Mundada, G., Aala, S., Sudarsa, D., Pandey, O.J., Chinnadurai, S., Matoba, O., Muniraj, I., Deshpande, A.: Deep learning-based hyperspectral microscopic imaging for cholangiocarcinoma detection and classification. Opt. Contin. 3(8), 1311\u20131324 (2024)","journal-title":"Opt. Contin."},{"key":"965_CR14","doi-asserted-by":"crossref","unstructured":"Deng, Y., Yin, J., Wang, Y., Chen, J., Sun, L., Li, Q.: Resnet-50 based method for cholangiocarcinoma identification from microscopic hyperspectral pathology images. In: Journal of Physics: Conference Series, vol. 1880, p. 012019 (2021). IOP Publishing","DOI":"10.1088\/1742-6596\/1880\/1\/012019"},{"key":"965_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.105520","volume":"146","author":"J Xie","year":"2022","unstructured":"Xie, J., Pu, X., He, J., Qiu, Y., Lu, C., Gao, W., Wang, X., Lu, H., Shi, J., Xu, Y., et al.: Survival prediction on intrahepatic cholangiocarcinoma with histomorphological analysis on the whole slide images. Comput. Biol. Med. 146, 105520 (2022)","journal-title":"Comput. Biol. Med."},{"key":"965_CR16","doi-asserted-by":"publisher","unstructured":"Sun, L., Zhou, M., Li, Q., Hu, M., Wen, Y., Zhang, J., Lu, Y., Chu, J.: Diagnosis of cholangiocarcinoma from microscopic hyperspectral pathological dataset by deep convolution neural networks. Methods 22\u201330 (2021). https:\/\/doi.org\/10.1016\/j.ymeth.2021.04.005","DOI":"10.1016\/j.ymeth.2021.04.005"},{"key":"965_CR17","doi-asserted-by":"publisher","first-page":"1237816","DOI":"10.3389\/fonc.2023.1237816","volume":"13","author":"H Zhou","year":"2023","unstructured":"Zhou, H., Li, J., Huang, J., Yue, Z.: A histopathological image classification method for cholangiocarcinoma based on spatial-channel feature fusion convolution neural network. Front. Oncol. 13, 1237816 (2023)","journal-title":"Front. Oncol."},{"issue":"5","key":"965_CR18","doi-asserted-by":"publisher","first-page":"1104","DOI":"10.3390\/genes14051104","volume":"14","author":"AA Shah","year":"2023","unstructured":"Shah, A.A., Alturise, F., Alkhalifah, T., Faisal, A., Khan, Y.D.: Edlm: ensemble deep learning model to detect mutation for the early detection of cholangiocarcinoma. Genes 14(5), 1104 (2023)","journal-title":"Genes"},{"issue":"5","key":"965_CR19","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1007\/s00138-023-01418-x","volume":"34","author":"H Gao","year":"2023","unstructured":"Gao, H., Yang, M., Cao, X., Liu, Q., Xu, P.: A high-level feature channel attention unet network for cholangiocarcinoma segmentation from microscopy hyperspectral images. Mach. Vis. Appl. 34(5), 72 (2023)","journal-title":"Mach. Vis. Appl."},{"issue":"3","key":"965_CR20","doi-asserted-by":"publisher","first-page":"737","DOI":"10.1016\/j.bbe.2022.05.009","volume":"42","author":"M Hammad","year":"2022","unstructured":"Hammad, M., Bakrey, M., Bakhiet, A., Tadeusiewicz, R., Abd El-Latif, A.A., P\u0142awiak, P.: A novel end-to-end deep learning approach for cancer detection based on microscopic medical images. Biocybern. Biomed. Eng. 42(3), 737\u2013748 (2022)","journal-title":"Biocybern. Biomed. Eng."},{"key":"965_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.optlastec.2023.109689","volume":"167","author":"C Li","year":"2023","unstructured":"Li, C., Wang, M., Sun, X., Zhu, M., Gao, H., Cao, X., Ullah, I., Liu, Q., Xu, P.: A novel dimensionality reduction algorithm for cholangiocarcinoma hyperspectral images. Opt. Laser Technol. 167, 109689 (2023)","journal-title":"Opt. Laser Technol."},{"issue":"2","key":"965_CR22","doi-asserted-by":"publisher","first-page":"775","DOI":"10.1007\/s12596-022-01089-3","volume":"53","author":"W El-Shafai","year":"2024","unstructured":"El-Shafai, W., Mahmoud, A.A., Ali, A.M., El-Rabaie, E.-S.M., Taha, T.E., El-Fishawy, A.S., Zahran, O., El-Samie, F.E.A.: Efficient classification of different medical image multimodalities based on simple cnn architecture and augmentation algorithms. J. Opt. 53(2), 775\u2013787 (2024)","journal-title":"J. Opt."},{"issue":"7","key":"965_CR23","doi-asserted-by":"publisher","first-page":"5930","DOI":"10.3390\/su15075930","volume":"15","author":"AW Salehi","year":"2023","unstructured":"Salehi, A.W., Khan, S., Gupta, G., Alabduallah, B.I., Almjally, A., Alsolai, H., Siddiqui, T., Mellit, A.: A study of cnn and transfer learning in medical imaging: Advantages, challenges, future scope. Sustainability 15(7), 5930 (2023)","journal-title":"Sustainability"},{"key":"965_CR24","doi-asserted-by":"crossref","unstructured":"Aburaed, N., Al-Saad, M., Zitouni, M.S., Alkhatib, M.Q., Wahbah, M., Halawani, Y., Panthakkan, A.: Cancer detection in hyperspectral imagery using artificial intelligence: Current trends and future directions. In: Artificial Intelligence for Medicine, pp. 133\u2013149. Elsevier (2024)","DOI":"10.1016\/B978-0-443-13671-9.00020-X"},{"key":"965_CR25","doi-asserted-by":"crossref","unstructured":"Banerjee, C., Mukherjee, T., Pasiliao\u00a0Jr, E.: An empirical study on generalizations of the relu activation function. In: Proceedings of the 2019 ACM Southeast Conference, pp. 164\u2013167 (2019)","DOI":"10.1145\/3299815.3314450"},{"issue":"1","key":"965_CR26","doi-asserted-by":"publisher","first-page":"5979","DOI":"10.1038\/s41598-022-09954-8","volume":"12","author":"SA Hicks","year":"2022","unstructured":"Hicks, S.A., Str\u00fcmke, I., Thambawita, V., Hammou, M., Riegler, M.A., Halvorsen, P., Parasa, S.: On evaluation metrics for medical applications of artificial intelligence. Sci. Rep. 12(1), 5979 (2022)","journal-title":"Sci. Rep."},{"key":"965_CR27","doi-asserted-by":"publisher","first-page":"1469293","DOI":"10.3389\/fonc.2024.1469293","volume":"14","author":"X Cao","year":"2024","unstructured":"Cao, X., Gao, H., Zhang, H., Fei, S., Xu, P., Wang, Z.: Mt-scnet: multi-scale token divided and spatial-channel fusion transformer network for microscopic hyperspectral image segmentation. Front. Oncol. 14, 1469293 (2024)","journal-title":"Front. Oncol."}],"container-title":["International Journal of Computational Intelligence Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-025-00965-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44196-025-00965-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-025-00965-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T11:02:38Z","timestamp":1757415758000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44196-025-00965-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,9]]},"references-count":27,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["965"],"URL":"https:\/\/doi.org\/10.1007\/s44196-025-00965-7","relation":{},"ISSN":["1875-6883"],"issn-type":[{"value":"1875-6883","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,9]]},"assertion":[{"value":"22 December 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 June 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 August 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 September 2025","order":4,"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 that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}}],"article-number":"230"}}