{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T12:09:18Z","timestamp":1779365358862,"version":"3.53.0"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"7","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.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100020771","name":"Young Scientists Fund of the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62502307"],"award-info":[{"award-number":["62502307"]}],"id":[{"id":"10.13039\/501100020771","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Shanghai Science and Technology Commission Research Project","award":["24YF2731300"],"award-info":[{"award-number":["24YF2731300"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Vis Comput"],"published-print":{"date-parts":[[2026,5]]},"DOI":"10.1007\/s00371-026-04439-5","type":"journal-article","created":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T12:43:59Z","timestamp":1778589839000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A weakly supervised framework for CT-based infectious pancreatic necrosis prediction with CAM-guided lesion localization"],"prefix":"10.1007","volume":"42","author":[{"given":"Ziyao","family":"Meng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiajia","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuechen","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Shen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziwei","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunfan","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Dong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haitao","family":"Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nan","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,12]]},"reference":[{"issue":"1","key":"4439_CR1","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1111\/1751-2980.12961","volume":"22","author":"P Alberti","year":"2021","unstructured":"Alberti, P., Pando, E., Mata, R., Vidal, L., Roson, N., Mast, R., Armario, D., Merino, X., Dopazo, C., Blanco, L., et al.: Evaluation of the modified computed tomography severity index (mctsi) and computed tomography severity index (ctsi) in predicting severity and clinical outcomes in acute pancreatitis. J. Dig. Dis. 22(1), 41\u201348 (2021)","journal-title":"J. Dig. Dis."},{"key":"4439_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.103519","volume":"74","author":"H Chen","year":"2022","unstructured":"Chen, H., Liu, Y., Shi, Z., Lyu, Y.: Pancreas segmentation by two-view feature learning and multi-scale supervision. Biomed. Signal Process. Control 74, 103519 (2022)","journal-title":"Biomed. Signal Process. Control"},{"key":"4439_CR3","unstructured":"Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., Zhou, Y.: Transunet: transformers make strong encoders for medical image segmentation. arXiv:2102.04306 (2021)"},{"key":"4439_CR4","doi-asserted-by":"crossref","unstructured":"Chen, Y., Jiang, M., Xia, C., Zhao, H., Ke, P., Chen, S., Ge, H., Li, K., Wang, X., Wang, Y., et\u00a0al.: A novel deep learning system for stemi prognostic prediction from multi-sequence cardiac magnetic resonance. Sci. Bull. (2025)","DOI":"10.1016\/j.scib.2025.11.027"},{"issue":"1","key":"4439_CR5","doi-asserted-by":"publisher","first-page":"e45","DOI":"10.1097\/MPA.0000000000002216","volume":"52","author":"Z Chen","year":"2023","unstructured":"Chen, Z., Wang, Y., Zhang, H., Yin, H., Hu, C., Huang, Z., Tan, Q., Song, B., Deng, L., Xia, Q.: Deep learning models for severity prediction of acute pancreatitis in the early phase from abdominal nonenhanced computed tomography images. Pancreas 52(1), e45\u2013e53 (2023)","journal-title":"Pancreas"},{"key":"4439_CR6","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1016\/j.neucom.2022.10.060","volume":"517","author":"S Dai","year":"2023","unstructured":"Dai, S., Zhu, Y., Jiang, X., Yu, F., Lin, J., Yang, D.: Td-net: trans-deformer network for automatic pancreas segmentation. Neurocomputing 517, 279\u2013293 (2023)","journal-title":"Neurocomputing"},{"key":"4439_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.104430","volume":"81","author":"Y Deng","year":"2023","unstructured":"Deng, Y., Lan, L., You, L., Chen, K., Peng, L., Zhao, W., Song, B., Wang, Y., Ji, Z., Zhou, X.: Automated ct pancreas segmentation for acute pancreatitis patients by combining a novel object detection approach and u-net. Biomed. Signal Process. Control 81, 104430 (2023)","journal-title":"Biomed. Signal Process. Control"},{"key":"4439_CR8","doi-asserted-by":"crossref","unstructured":"Gao, L., Zhang, H., Li, G., Ye, B., Zhou, J., Tong, Z., Ke, L., Windsor, J.A., Li, W., CAPCTG, C.A.P.C.T.G.: The clinical outcome from early versus delayed minimally invasive intervention for infected pancreatic necrosis: a systematic review and meta-analysis. J. Gastroenterol. 57(6), 397\u2013406 (2022)","DOI":"10.1007\/s00535-022-01876-6"},{"key":"4439_CR9","unstructured":"Gu, A., Dao, T.: Mamba: Linear-time sequence modeling with selective state spaces. arXiv:2312.00752 (2023)"},{"key":"4439_CR10","doi-asserted-by":"crossref","unstructured":"Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H.\u00a0R., Xu, D.: Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images. In: International MICCAI Brainlesion Workshop, pp. 272\u2013284. Springer (2021)","DOI":"10.1007\/978-3-031-08999-2_22"},{"key":"4439_CR11","doi-asserted-by":"crossref","unstructured":"Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.\u00a0R., Xu, D.: Unetr: transformers for 3d medical image segmentation. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 574\u2013584 (2022)","DOI":"10.1109\/WACV51458.2022.00181"},{"issue":"5","key":"4439_CR12","doi-asserted-by":"publisher","first-page":"1601","DOI":"10.1109\/JBHI.2020.3023462","volume":"25","author":"P Hu","year":"2020","unstructured":"Hu, P., Li, X., Tian, Y., Tang, T., Zhou, T., Bai, X., Zhu, S., Liang, T., Li, J.: Automatic pancreas segmentation in ct images with distance-based saliency-aware denseaspp network. IEEE J. Biomed. Health Inform. 25(5), 1601\u20131611 (2020)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"4439_CR13","doi-asserted-by":"publisher","first-page":"1509915","DOI":"10.3389\/fmicb.2024.1509915","volume":"15","author":"B Huang","year":"2024","unstructured":"Huang, B., Gao, Y., Wu, L.: Assessment of body composition and prediction of infectious pancreatic necrosis via non-contrast ct radiomics and deep learning. Front. Microbiol. 15, 1509915 (2024)","journal-title":"Front. Microbiol."},{"issue":"2","key":"4439_CR14","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","volume":"18","author":"F Isensee","year":"2021","unstructured":"Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H.: nnu-net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18(2), 203\u2013211 (2021)","journal-title":"Nat. Methods"},{"key":"4439_CR15","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1016\/j.media.2019.02.009","volume":"54","author":"H Kervadec","year":"2019","unstructured":"Kervadec, H., Dolz, J., Tang, M., Granger, E., Boykov, Y., Ayed, I.B.: Constrained-cnn losses for weakly supervised segmentation. Med. Image Anal. 54, 88\u201399 (2019)","journal-title":"Med. Image Anal."},{"issue":"1","key":"4439_CR16","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1002\/mp.14617","volume":"48","author":"W Li","year":"2021","unstructured":"Li, W., Qin, S., Li, F., Wang, L.: Mad-unet: a deep u-shaped network combined with an attention mechanism for pancreas segmentation in ct images. Med. Phys. 48(1), 329\u2013341 (2021)","journal-title":"Med. Phys."},{"key":"4439_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102315","volume":"76","author":"Y Li","year":"2022","unstructured":"Li, Y., Liu, Y., Huang, L., Wang, Z., Luo, J.: Deep weakly-supervised breast tumor segmentation in ultrasound images with explicit anatomical constraints. Med. Image Anal. 76, 102315 (2022)","journal-title":"Med. Image Anal."},{"issue":"1","key":"4439_CR18","doi-asserted-by":"publisher","first-page":"17514","DOI":"10.1038\/s41598-023-44828-7","volume":"13","author":"H Liang","year":"2023","unstructured":"Liang, H., Wang, M., Wen, Y., Du, F., Jiang, L., Geng, X., Tang, L., Yan, H.: Predicting acute pancreatitis severity with enhanced computed tomography scans using convolutional neural networks. Sci. Rep. 13(1), 17514 (2023)","journal-title":"Sci. Rep."},{"issue":"3","key":"4439_CR19","doi-asserted-by":"publisher","first-page":"350","DOI":"10.1016\/j.pan.2024.02.003","volume":"24","author":"W Lin","year":"2024","unstructured":"Lin, W., Huang, Y., Zhu, J., Sun, H., Su, N., Pan, J., Xu, J., Chen, L.: Machine learning improves early prediction of organ failure in hyperlipidemia acute pancreatitis using clinical and abdominal ct features. Pancreatology 24(3), 350\u2013356 (2024)","journal-title":"Pancreatology"},{"issue":"4","key":"4439_CR20","doi-asserted-by":"publisher","first-page":"2891","DOI":"10.1007\/s00371-024-03574-1","volume":"41","author":"X Liu","year":"2025","unstructured":"Liu, X., Huang, G., Yuan, X., Zheng, Z., Zhong, G., Chen, X., Pun, C.-M.: Weakly supervised semantic segmentation via saliency perception with uncertainty-guided noise suppression. Vis. Comput. 41(4), 2891\u20132906 (2025)","journal-title":"Vis. Comput."},{"key":"4439_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108341","volume":"122","author":"X Liu","year":"2022","unstructured":"Liu, X., Yuan, Q., Gao, Y., He, K., Wang, S., Tang, X., Tang, J., Shen, D.: Weakly supervised segmentation of covid19 infection with scribble annotation on ct images. Pattern Recogn. 122, 108341 (2022)","journal-title":"Pattern Recogn."},{"key":"4439_CR22","unstructured":"Ma, J., Li, F., Wang, B.: U-mamba: Enhancing long-range dependency for biomedical image segmentation. arXiv:2401.04722 (2024)"},{"issue":"1","key":"4439_CR23","doi-asserted-by":"publisher","DOI":"10.1148\/ryai.2019180019","volume":"1","author":"B Marinelli","year":"2019","unstructured":"Marinelli, B., Kang, M., Martini, M., Zech, J.R., Titano, J., Cho, S., Costa, A.B., Oermann, E.K.: Combination of active transfer learning and natural language processing to improve liver volumetry using surrogate metrics with deep learning. Radiol. Artif. Intell. 1(1), e180019 (2019)","journal-title":"Radiol. Artif. Intell."},{"issue":"4","key":"4439_CR24","doi-asserted-by":"publisher","first-page":"382","DOI":"10.1001\/jama.2020.20317","volume":"325","author":"MA Mederos","year":"2021","unstructured":"Mederos, M.A., Reber, H.A., Girgis, M.D.: Acute pancreatitis: a review. JAMA 325(4), 382\u2013390 (2021)","journal-title":"JAMA"},{"key":"4439_CR25","doi-asserted-by":"crossref","unstructured":"Meng, Z., Qin, Y., Wang, T., Shen, H., Liu, Y., Dong, J., Song, H.: Pmdc: pancreas segmentation with mamba block and deformable 3d convolution. In: 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 2303\u20132310. IEEE (2024)","DOI":"10.1109\/BIBM62325.2024.10822522"},{"issue":"1","key":"4439_CR26","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.232085","volume":"312","author":"L Misera","year":"2024","unstructured":"Misera, L., M\u00fcller-Franzes, G., Truhn, D., Kather, J.N.: Weakly supervised deep learning in radiology. Radiology 312(1), e232085 (2024)","journal-title":"Radiology"},{"key":"4439_CR27","doi-asserted-by":"publisher","first-page":"12535","DOI":"10.1007\/s00521-020-04710-3","volume":"32","author":"J Mo","year":"2020","unstructured":"Mo, J., Zhang, L., Wang, Y., Huang, H.: Iterative 3d feature enhancement network for pancreas segmentation from ct images. Neural Comput. Appl. 32, 12535\u201312546 (2020)","journal-title":"Neural Comput. Appl."},{"key":"4439_CR28","doi-asserted-by":"crossref","unstructured":"Ning, C., Ouyang, H., Xiao, J., Wu, D., Sun, Z., Liu, B., Shen, D., Hong, X., Lin, C., Li, J., et al.: Development and validation of an explainable machine learning model for mortality prediction among patients with infected pancreatic necrosis. EClinicalMedicine 80 (2025)","DOI":"10.1016\/j.eclinm.2025.103074"},{"key":"4439_CR29","doi-asserted-by":"crossref","unstructured":"Podda, M., Pellino, G., Di Saverio, S., Coccolini, F., Pacella, D., Cioffi, S.P.B., Virdis, F., Balla, A., Ielpo, B., Pata, F., et al.: Infected pancreatic necrosis: outcomes and clinical predictors of mortality. a post hoc analysis of the manctra-1 international study. Updat. Surg. 75(3), 493\u2013522 (2023)","DOI":"10.1007\/s13304-023-01488-6"},{"issue":"1","key":"4439_CR30","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1186\/s12880-024-01509-9","volume":"24","author":"M Qi","year":"2024","unstructured":"Qi, M., Lu, C., Dai, R., Zhang, J., Hu, H., Shan, X.: Prediction of acute pancreatitis severity based on early ct radiomics. BMC Med. Imaging 24(1), 321 (2024)","journal-title":"BMC Med. Imaging"},{"key":"4439_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.104173","volume":"79","author":"C Qiu","year":"2023","unstructured":"Qiu, C., Liu, Z., Song, Y., Yin, J., Han, K., Zhu, Y., Liu, Y., Sheng, V.S.: Rtunet: residual transformer unet specifically for pancreas segmentation. Biomed. Signal Process. Control 79, 104173 (2023)","journal-title":"Biomed. Signal Process. Control"},{"key":"4439_CR32","doi-asserted-by":"crossref","unstructured":"Raghuwanshi, S., Gupta, R., Vyas, M.M., Sharma, R.: Ct evaluation of acute pancreatitis and its prognostic correlation with ct severity index. J. Clin. Diagnost. Res. JCDR 10(6), TC06 (2016)","DOI":"10.7860\/JCDR\/2016\/19849.7934"},{"key":"4439_CR33","doi-asserted-by":"publisher","first-page":"1061402","DOI":"10.3389\/fradi.2022.1061402","volume":"2","author":"S Rajapaksa","year":"2022","unstructured":"Rajapaksa, S., Khalvati, F.: Relevance maps: a weakly supervised segmentation method for 3d brain tumours in mris. Front. Radiol. 2, 1061402 (2022)","journal-title":"Front. Radiol."},{"key":"4439_CR34","doi-asserted-by":"crossref","unstructured":"Roth, H.R., Lu, L., Farag, A., Shin, H.-C., Liu, J., Turkbey, E.B., Summers, R.M.: Deeporgan: multi-level deep convolutional networks for automated pancreas segmentation. In: Medical Image Computing and Computer-Assisted Intervention-MICCAI 2015: 18th International Conference, Munich, Germany, October 5\u20139, 2015, Proceedings, Part I 18, pp. 556\u2013564. Springer (2015)","DOI":"10.1007\/978-3-319-24553-9_68"},{"key":"4439_CR35","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1016\/j.media.2018.01.006","volume":"45","author":"HR Roth","year":"2018","unstructured":"Roth, H.R., Lu, L., Lay, N., Harrison, A.P., Farag, A., Sohn, A., Summers, R.M.: Spatial aggregation of holistically-nested convolutional neural networks for automated pancreas localization and segmentation. Med. Image Anal. 45, 94\u2013107 (2018)","journal-title":"Med. Image Anal."},{"issue":"1","key":"4439_CR36","doi-asserted-by":"publisher","first-page":"12495","DOI":"10.1038\/s41598-019-48995-4","volume":"9","author":"L Shen","year":"2019","unstructured":"Shen, L., Margolies, L.R., Rothstein, J.H., Fluder, E., McBride, R., Sieh, W.: Deep learning to improve breast cancer detection on screening mammography. Sci. Rep. 9(1), 12495 (2019)","journal-title":"Sci. Rep."},{"key":"4439_CR37","unstructured":"Simpson, A.L., Antonelli, M., Bakas, S., Bilello, M., Farahani, K., Van\u00a0Ginneken, B., Kopp-Schneider, A., Landman, B.A., Litjens, G., Menze, B., et\u00a0al.: A large annotated medical image dataset for the development and evaluation of segmentation algorithms. arXiv:1902.09063 (2019)"},{"issue":"12","key":"4439_CR38","doi-asserted-by":"publisher","first-page":"1251","DOI":"10.1007\/s40265-022-01766-4","volume":"82","author":"P Szatmary","year":"2022","unstructured":"Szatmary, P., Grammatikopoulos, T., Cai, W., Huang, W., Mukherjee, R., Halloran, C., Beyer, G., Sutton, R.: Acute pancreatitis: diagnosis and treatment. Drugs 82(12), 1251\u20131276 (2022)","journal-title":"Drugs"},{"key":"4439_CR39","doi-asserted-by":"crossref","unstructured":"Tushar, F.I., D\u2019Anniballe, V.M., Hou, R., Mazurowski, M.A., Fu, W., Samei, E., Rubin, G.D., Lo, J.Y.: Classification of multiple diseases on body ct scans using weakly supervised deep learning. Radiol. Artif. Intell. 4(1), e210026 (2021)","DOI":"10.1148\/ryai.210026"},{"key":"4439_CR40","doi-asserted-by":"crossref","unstructured":"Viniavskyi, O., Dobko, M., Dobosevych, O.: Weakly-supervised segmentation for disease localization in chest x-ray images. In: International Conference on Artificial Intelligence in Medicine, pp. 249\u2013259. Springer (2020)","DOI":"10.1007\/978-3-030-59137-3_23"},{"key":"4439_CR41","doi-asserted-by":"crossref","unstructured":"Wang, S., Li, C., Wang, R., Liu, Z., Wang, M., Tan, H., Wu, Y., Liu, X., Sun, H., Yang, R., et al.: Annotation-efficient deep learning for automatic medical image segmentation. Nat. Commun. 12(1), 5915 (2021)","DOI":"10.1038\/s41467-021-26216-9"},{"issue":"1","key":"4439_CR42","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1186\/s12876-022-02490-9","volume":"22","author":"ML Wiese","year":"2022","unstructured":"Wiese, M.L., Urban, S., Von Rheinbaben, S., Frost, F., Sendler, M., Weiss, F.U., B\u00fclow, R., Kromrey, M.-L., Tran, Q.T., Lerch, M.M., et al.: Identification of early predictors for infected necrosis in acute pancreatitis. BMC Gastroenterol. 22(1), 405 (2022)","journal-title":"BMC Gastroenterol."},{"issue":"5","key":"4439_CR43","doi-asserted-by":"publisher","first-page":"3553","DOI":"10.1007\/s00371-024-03619-5","volume":"41","author":"Y Wu","year":"2025","unstructured":"Wu, Y., Fang, P., Wang, X., Shen, J.: Predicting pancreatic diseases from fundus images using deep learning. Vis. Comput. 41(5), 3553\u20133564 (2025)","journal-title":"Vis. Comput."},{"key":"4439_CR44","doi-asserted-by":"crossref","unstructured":"Xing, Z., Ye, T., Yang, Y., Liu, G., Zhu, L.: Segmamba: long-range sequential modeling mamba for 3d medical image segmentation. arXiv:2401.13560 (2024)","DOI":"10.1007\/978-3-031-72111-3_54"},{"issue":"6","key":"4439_CR45","doi-asserted-by":"publisher","first-page":"827","DOI":"10.1016\/j.pan.2024.07.001","volume":"24","author":"T Yamamoto","year":"2024","unstructured":"Yamamoto, T., Horibe, M., Sanui, M., Sasaki, M., Mizobata, Y., Esaki, M., Sawano, H., Goto, T., Ikeura, T., Takeda, T., et al.: Early detection of necrosis in low-enhanced pancreatic parenchyma using contrast-enhanced computed tomography was a better predictor of clinical outcomes than pancreatic inflammation: A multicentric cohort study of severe acute pancreatitis. Pancreatology 24(6), 827\u2013833 (2024)","journal-title":"Pancreatology"},{"key":"4439_CR46","doi-asserted-by":"crossref","unstructured":"Yao, Q., Duan, Y., Jin, C., Li, X., Wei, S., Shi, Y., Zhang, Y., Zhang, J., Liu, C.: A nomogram for early prediction of infected pancreatic necrosis based on contrast-enhanced ct radiomics and inflammatory indicators. J. Inflamm. Res. 13651\u201313663 (2025)","DOI":"10.2147\/JIR.S538345"},{"key":"4439_CR47","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2024.105341","volume":"184","author":"M Yin","year":"2024","unstructured":"Yin, M., Lin, J., Wang, Y., Liu, Y., Zhang, R., Duan, W., Zhou, Z., Zhu, S., Gao, J., Liu, L., et al.: Development and validation of a multimodal model in predicting severe acute pancreatitis based on radiomics and deep learning. Int. J. Med. Inform. 184, 105341 (2024)","journal-title":"Int. J. Med. Inform."},{"issue":"1","key":"4439_CR48","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1186\/s12880-024-01339-9","volume":"24","author":"C Zhang","year":"2024","unstructured":"Zhang, C., Peng, J., Wang, L., Wang, Y., Chen, W., Sun, M.-W., Jiang, H.: A deep learning-powered diagnostic model for acute pancreatitis. BMC Med. Imaging 24(1), 154 (2024)","journal-title":"BMC Med. Imaging"},{"key":"4439_CR49","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107762","volume":"114","author":"D Zhang","year":"2021","unstructured":"Zhang, D., Zhang, J., Zhang, Q., Han, J., Zhang, S., Han, J.: Automatic pancreas segmentation based on lightweight dcnn modules and spatial prior propagation. Pattern Recogn. 114, 107762 (2021)","journal-title":"Pattern Recogn."},{"key":"4439_CR50","unstructured":"Zhu, L., Liao, B., Zhang, Q., Wang, X., Liu, W., Wang, X.: Vision mamba: efficient visual representation learning with bidirectional state space model. arXiv:2401.09417 (2024)"},{"key":"4439_CR51","doi-asserted-by":"crossref","unstructured":"Zhu, Q.-Y., Li, R.-M., Zhu, Y.-P., Hao, D.-L., Liu, Y., Yu, J., Yang, X., Zhang, Y.-S., Lin, T.-J., Yan, X., et al.: Early predictors of infected pancreatic necrosis in severe acute pancreatitis: implications of neutrophil to lymphocyte ratio, blood procalcitonin concentration, and modified ct severity index. Dig. Dis. 41(4), 677\u2013684 (2023)","DOI":"10.1159\/000529366"}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-026-04439-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-026-04439-5","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-026-04439-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T11:42:54Z","timestamp":1779363774000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-026-04439-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5]]},"references-count":51,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2026,5]]}},"alternative-id":["4439"],"URL":"https:\/\/doi.org\/10.1007\/s00371-026-04439-5","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"value":"0178-2789","type":"print"},{"value":"1432-2315","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5]]},"assertion":[{"value":"11 February 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 March 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 May 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 Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"294"}}