{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,3]],"date-time":"2026-10-03T00:56:56Z","timestamp":1790989016630,"version":"4.1.0"},"reference-count":59,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2025,7,8]],"date-time":"2025-07-08T00:00:00Z","timestamp":1751932800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,7,8]],"date-time":"2025-07-08T00:00:00Z","timestamp":1751932800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging. Inform. med."],"DOI":"10.1007\/s10278-025-01602-7","type":"journal-article","created":{"date-parts":[[2025,7,8]],"date-time":"2025-07-08T10:01:49Z","timestamp":1751968909000},"page":"1352-1370","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Vision Transformers-Based Deep Feature Generation Framework for Hydatid Cyst Classification in Computed Tomography Images"],"prefix":"10.1007","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3237-4536","authenticated-orcid":false,"given":"Metin","family":"Sagik","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2993-5769","authenticated-orcid":false,"given":"Abdurrahman","family":"Gumus","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,7,8]]},"reference":[{"issue":"4","key":"1602_CR1","first-page":"327","volume":"15","author":"S S\u00f6zen","year":"2011","unstructured":"S\u00f6zen, S., Emir, S., T\u00fckenmez, M., Topuz, \u00d6.: The results of surgical treatment for hepatic hydatid disease. Hippokratia. 15(4), 327 (2011)","journal-title":"Hippokratia."},{"issue":"1","key":"1602_CR2","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1002\/jum.15691","volume":"41","author":"M Wu","year":"2022","unstructured":"Wu, M., Yan, C., Wang, X., Liu, Q., Liu, Z., Song, T.: Automatic classification of hepatic cystic echinococcosis using ultrasound images and deep learning. J Ultrasound Med. 41(1), 163\u2013174 (2022) 10.1002\/jum.15691","journal-title":"J Ultrasound Med."},{"issue":"2","key":"1602_CR3","doi-asserted-by":"publisher","first-page":"119","DOI":"10.2478\/helm-2021-0022","volume":"58","author":"F Ch\u00e1vez-Ruvalcaba","year":"2021","unstructured":"Ch\u00e1vez-Ruvalcaba, F., Ch\u00e1vez-Ruvalcaba, M., Santiba\u00f1ez, K.M., Mu\u00f1oz-Carrillo, J., Coria, A.L., Mart\u00ednez, R.R.: Foodborne parasitic diseases in the neotropics-a review. Helminthologia. 58(2), 119\u2013133 (2021) https:\/\/doi.org\/10.2478\/helm-2021-0022","journal-title":"Helminthologia."},{"issue":"4","key":"1602_CR4","doi-asserted-by":"publisher","first-page":"883","DOI":"10.1378\/chest.13-2072","volume":"145","author":"D Khemasuwan","year":"2014","unstructured":"Khemasuwan, D., Farver, C.F., Mehta, A.C.: Parasites of the air passages. Chest. 145(4), 883\u2013895 (2014) https:\/\/doi.org\/10.1378\/chest.13-2072","journal-title":"Chest."},{"issue":"2","key":"1602_CR5","first-page":"174","volume":"33","author":"A Acar","year":"2009","unstructured":"Acar, A., Rodop, O., Yenilmez, E., Baylan, O., Onc\u00fcl, O.: Case report: primary localization of a hydatid cyst in the adductor brevis muscle. Turkiye Parazitol Derg. 33(2), 174\u2013176 (2009)","journal-title":"Turkiye Parazitol Derg."},{"issue":"3","key":"1602_CR6","doi-asserted-by":"publisher","first-page":"751","DOI":"10.1007\/s00381-022-05770-7","volume":"39","author":"L Padayachy","year":"2023","unstructured":"Padayachy, L., Ozek, M.: Hydatid disease of the brain and spine. Childs Nerv Syst. 39(3), 751\u2013758 (2023) https:\/\/doi.org\/10.1007\/s00381-022-05770-7","journal-title":"Childs Nerv Syst."},{"key":"1602_CR7","doi-asserted-by":"publisher","unstructured":"Derbel, F., Mabrouk, M.B., Hamida, M.B.H., Mazhoud, J., Youssef, S., Ali, A.B., Jemni, H., Mama, N., Ibtissem, H., Nadia, A., Ouni, C.E., Naija, W., Mokni, M., Hamida, R.B.H.: Hydatid cysts of the liver diagnosis, complications and treatment. In: Derbel, F. (ed.) Abdominal Surgery. IntechOpen, Rijeka (2012). Chap. 5. https:\/\/doi.org\/10.5772\/48433","DOI":"10.5772\/48433"},{"issue":"13","key":"1602_CR8","doi-asserted-by":"publisher","first-page":"1438","DOI":"10.3748\/wjg.v18.i13.1438","volume":"18","author":"G Marrone","year":"2012","unstructured":"Marrone, G., Caruso, S., Mamone, G., Carollo, V., Milazzo, M., Gruttadauria, S., Luca, A., Gridelli, B., et al.: Multidisciplinary imaging of liver hydatidosis. World J Gastroenterol. 18(13), 1438\u20131447 (2012) https:\/\/doi.org\/10.3748\/wjg.v18.i13.1438","journal-title":"World J Gastroenterol."},{"issue":"2","key":"1602_CR9","doi-asserted-by":"publisher","first-page":"459","DOI":"10.1148\/radiology.139.2.7220891","volume":"139","author":"HA Gharbi","year":"1981","unstructured":"Gharbi, H.A., Hassine, W., Brauner, M.W., Dupuch, K.: Ultrasound examination of the hydatic liver. Radiology. 139(2), 459\u2013463 (1981) https:\/\/doi.org\/10.1148\/radiology.139.2.7220891","journal-title":"Radiology."},{"key":"1602_CR10","doi-asserted-by":"publisher","unstructured":"Al-Ani, I.M., Mahdi, M.B., Khalaf, G.M.: Application of ultrasound classification of hepatic hydatid cyst in iraqi population. Al-Anbar Medical Journal. 16(1), 3\u20137 (2020) https:\/\/doi.org\/10.33091\/amj.2020.170928","DOI":"10.33091\/amj.2020.170928"},{"key":"1602_CR11","doi-asserted-by":"publisher","unstructured":"Xin, S., Shi, H., Jide, A., Zhu, M., Ma, C., Liao, H.: Automatic lesion segmentation and classification of hepatic echinococcosis using a multiscale-feature convolutional neural network. Med Biol Eng Comput. 58, 659\u2013668 (2020) https:\/\/doi.org\/10.1007\/s11517-020-02126-8","DOI":"10.1007\/s11517-020-02126-8"},{"key":"1602_CR12","doi-asserted-by":"publisher","unstructured":"To\u011fa\u00e7ar, M., C\u00f6mert, Z., Ergen, B.: Intelligent skin cancer detection applying autoencoder, MobileNetV2 and spiking neural networks. Chaos Solitons Fractals. 144, 110714 (2021) https:\/\/doi.org\/10.1016\/j.chaos.2021.110714","DOI":"10.1016\/j.chaos.2021.110714"},{"issue":"36","key":"1602_CR13","doi-asserted-by":"publisher","first-page":"23113","DOI":"10.1007\/s00521-024-10457-y","volume":"36","author":"Z C\u00f6mert","year":"2024","unstructured":"C\u00f6mert, Z., Sbrollini, A., Demircan, F., Burattini, L.: Computerized otoscopy image-based artificial intelligence model utilizing deep features provided by vision transformer, grid search optimization, and support vector machine for otitis media diagnosis. Neural Comput Appl. 36(36), 23113\u201323129 (2024) https:\/\/doi.org\/10.1007\/s00521-024-10457-y","journal-title":"Neural Comput Appl."},{"issue":"1","key":"1602_CR14","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1007\/s10278-023-00911-z","volume":"37","author":"K Abinaya","year":"2024","unstructured":"Abinaya, K., Sivakumar, B.: A deep learning-based approach for cervical cancer classification using 3D CNN and vision transformer. J Imaging Inform Med. 37(1), 280 (2024) https:\/\/doi.org\/10.1007\/s10278-023-00911-z","journal-title":"J Imaging Inform Med."},{"key":"1602_CR15","doi-asserted-by":"publisher","unstructured":"Paraddy, S., Virupakshappa: Addressing challenges in skin cancer diagnosis: A convolutional swin transformer approach. J Imaging Inform Med, 1\u201321 (2024) https:\/\/doi.org\/10.1007\/s10278-024-01290-9","DOI":"10.1007\/s10278-024-01290-9"},{"issue":"11","key":"1602_CR16","doi-asserted-by":"publisher","first-page":"7179","DOI":"10.1007\/s00500-023-07945-z","volume":"27","author":"Y Gul","year":"2023","unstructured":"Gul, Y., Muezzinoglu, T., Kilicarslan, G., Dogan, S., Tuncer, T.: Application of the deep transfer learning framework for hydatid cyst classification using CT images. Soft Comput. 27(11), 7179\u20137189 (2023) https:\/\/doi.org\/10.1007\/s00500-023-07945-z","journal-title":"Soft Comput."},{"key":"1602_CR17","doi-asserted-by":"publisher","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-CAM: visual explanations from deep networks via gradient-based localization. Int J Comput Vis. 128, 336\u2013359 (2020) https:\/\/doi.org\/10.1007\/S11263-019-01228-7","DOI":"10.1007\/S11263-019-01228-7"},{"key":"1602_CR18","doi-asserted-by":"publisher","unstructured":"Jahmunah, V., Ng, E.Y., Tan, R.-S., Oh, S.L., Acharya, U.R.: Explainable detection of myocardial infarction using deep learning models with Grad-CAM technique on ECG signals. Comput Biol Med. 146, 105550 (2022) https:\/\/doi.org\/10.1016\/J.COMPBIOMED.2022.105550","DOI":"10.1016\/J.COMPBIOMED.2022.105550"},{"key":"1602_CR19","doi-asserted-by":"publisher","unstructured":"Tasci, B., Tasci, I.: Deep feature extraction based brain image classification model using preprocessed images: PDRNet. Biomed Signal Process Control. 78, 103948 (2022) https:\/\/doi.org\/10.1016\/J.BSPC.2022.103948","DOI":"10.1016\/J.BSPC.2022.103948"},{"issue":"17","key":"1602_CR20","doi-asserted-by":"publisher","first-page":"9926","DOI":"10.3390\/app13179926","volume":"13","author":"M Yildirim","year":"2023","unstructured":"Yildirim, M.: Image visualization and classification using hydatid cyst images with an explainable hybrid model. Appl Sci. 13(17), 9926 (2023) https:\/\/doi.org\/10.3390\/app13179926","journal-title":"Appl Sci."},{"key":"1602_CR21","unstructured":"Arnold, V.I., Avez, A.: Ergodic problems of classical mechanics. (No Title). (1968) https:\/\/cir.nii.ac.jp\/crid\/1130282273312514176"},{"key":"1602_CR22","doi-asserted-by":"publisher","unstructured":"Bao, J., Yang, Q.: Period of the discrete arnold cat map and general cat map. Nonlinear Dyn. 70, 1365\u20131375 (2012) https:\/\/doi.org\/10.1007\/S11071-012-0539-3","DOI":"10.1007\/S11071-012-0539-3"},{"issue":"2","key":"1602_CR23","doi-asserted-by":"publisher","first-page":"1883","DOI":"10.4249\/SCHOLARPEDIA.1883","volume":"4","author":"LE Peterson","year":"2009","unstructured":"Peterson, L.E.: K-nearest neighbor. Scholarpedia. 4(2), 1883 (2009) https:\/\/doi.org\/10.4249\/SCHOLARPEDIA.1883","journal-title":"K-nearest neighbor. Scholarpedia."},{"issue":"4","key":"1602_CR24","doi-asserted-by":"publisher","first-page":"1675","DOI":"10.1007\/S10278-023-00827-8","volume":"36","author":"M Erten","year":"2023","unstructured":"Erten, M., Tuncer, I., Barua, P.D., Yildirim, K., Dogan, S., Tuncer, T., Tan, R.-S., Fujita, H., Acharya, U.R.: Automated urine cell image classification model using chaotic mixer deep feature extraction. J Digit Imaging. 36(4), 1675\u20131686 (2023) https:\/\/doi.org\/10.1007\/S10278-023-00827-8","journal-title":"J Digit Imaging."},{"key":"1602_CR25","doi-asserted-by":"publisher","unstructured":"Poyraz, A.K., Dogan, S., Akbal, E., Tuncer, T.: Automated brain disease classification using exemplar deep features. Biomed Signal Process Control. 73, https:\/\/doi.org\/10.1016\/j.bspc.2021.103448","DOI":"10.1016\/j.bspc.2021.103448"},{"key":"1602_CR26","doi-asserted-by":"publisher","unstructured":"Aslan, N., Koca, G.O., Kobat, M.A., Dogan, S.: Multi-classification deep CNN model for diagnosing COVID-19 using iterative neighborhood component analysis and iterative ReliefF feature selection techniques with X-ray images. Chemom Intell Lab Syst. 224, 104539 (2022) https:\/\/doi.org\/10.1016\/j.chemolab.2022.104539","DOI":"10.1016\/j.chemolab.2022.104539"},{"issue":"2","key":"1602_CR27","doi-asserted-by":"publisher","first-page":"181","DOI":"10.3390\/bioengineering12020181","volume":"12","author":"T Tegshee","year":"2025","unstructured":"Tegshee, T., Dorjsuren, T., Lee, S., Batjargal, D.: A study on staging cystic echinococcosis using machine learning methods. Bioengineering. 12(2), 181 (2025)","journal-title":"Bioengineering."},{"key":"1602_CR28","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems. 30 (2017)"},{"key":"1602_CR29","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929. (2020)"},{"issue":"10s","key":"1602_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3505244","volume":"54","author":"S Khan","year":"2022","unstructured":"Khan, S., Naseer, M., Hayat, M., Zamir, S.W., Khan, F.S., Shah, M.: Transformers in vision: A survey. ACM Comput Surv. 54(10s), 1\u201341 (2022) https:\/\/doi.org\/10.1145\/3505244","journal-title":"ACM Comput Surv."},{"key":"1602_CR31","doi-asserted-by":"publisher","unstructured":"Tu, Z., Talebi, H., Zhang, H., Yang, F., Milanfar, P., Bovik, A., Li, Y.: MaxViT: Multi-axis vision transformer. In: Avidan, S., Brostow, G., Ciss\u00e8, M., Farinella, G.M., Hassner, T. (eds.) Computer Vision \u2013 ECCV 2022. Lecture Notes in Computer Science, vol. 13664, pp. 459\u2013479. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-20053-3_27","DOI":"10.1007\/978-3-031-20053-3_27"},{"key":"1602_CR32","doi-asserted-by":"publisher","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 10012\u201310022 (2021). https:\/\/doi.org\/10.1109\/ICCV48922.2021.00986","DOI":"10.1109\/ICCV48922.2021.00986"},{"issue":"9","key":"1602_CR33","doi-asserted-by":"publisher","first-page":"3579","DOI":"10.1007\/s13042-024-02110-w","volume":"15","author":"I Pacal","year":"2024","unstructured":"Pacal, I.: A novel swin transformer approach utilizing residual multi-layer perceptron for diagnosing brain tumors in mri images. International Journal of Machine Learning and Cybernetics. 15(9), 3579\u20133597 (2024) https:\/\/doi.org\/10.1007\/s13042-024-02110-w","journal-title":"International Journal of Machine Learning and Cybernetics."},{"key":"1602_CR34","doi-asserted-by":"publisher","unstructured":"Zhang, C., Wang, L., Cheng, S., Li, Y.: SwinSUNet: Pure transformer network for remote sensing image change detection. IEEE Trans Geosci Remote Sens. 60, 1\u201313 (2022) https:\/\/doi.org\/10.1109\/TGRS.2022.3160007","DOI":"10.1109\/TGRS.2022.3160007"},{"issue":"12","key":"1602_CR35","doi-asserted-by":"publisher","first-page":"2861","DOI":"10.3390\/rs14122861","volume":"14","author":"H Gong","year":"2022","unstructured":"Gong, H., Mu, T., Li, Q., Dai, H., Li, C., He, Z., Wang, W., Han, F., Tuniyazi, A., Li, H., et al.: Swin-transformer-enabled YOLOv5 with attention mechanism for small object detection on satellite images. Remote Sens. 14(12), 2861 (2022) https:\/\/doi.org\/10.3390\/rs14122861","journal-title":"Remote Sens."},{"key":"1602_CR36","doi-asserted-by":"publisher","unstructured":"Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205\u2013218 (2022). https:\/\/doi.org\/10.1007\/978-3-031-25066-8_9 . Springer","DOI":"10.1007\/978-3-031-25066-8_9"},{"key":"1602_CR37","doi-asserted-by":"publisher","unstructured":"Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H.R., Xu, D.: Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images. In: International MICCAI Brainlesion Workshop, pp. 272\u2013284 (2021). https:\/\/doi.org\/10.1007\/978-3-031-08999-2_22 . Springer","DOI":"10.1007\/978-3-031-08999-2_22"},{"key":"1602_CR38","unstructured":"Goldberger, J., Hinton, G.E., Roweis, S., Salakhutdinov, R.R.: Neighbourhood components analysis. Advances in neural information processing systems. 17 (2004)"},{"key":"1602_CR39","doi-asserted-by":"publisher","unstructured":"Liu, H., Cui, G., Luo, Y., Guo, Y., Zhao, L., Wang, Y., Subasi, A., Dogan, S., Tuncer, T.: Artificial intelligence-based breast cancer diagnosis using ultrasound images and grid-based deep feature generator. International Journal of General Medicine, 2271\u20132282 (2022) https:\/\/doi.org\/10.2147\/IJGM.S347491","DOI":"10.2147\/IJGM.S347491"},{"issue":"1","key":"1602_CR40","doi-asserted-by":"publisher","first-page":"17297","DOI":"10.1038\/s41598-022-21380-4","volume":"12","author":"PD Barua","year":"2022","unstructured":"Barua, P.D., Baygin, N., Dogan, S., Baygin, M., Arunkumar, N., Fujita, H., Tuncer, T., Tan, R.-S., Palmer, E., Azizan, M.M.B., et al.: Automated detection of pain levels using deep feature extraction from shutter blinds-based dynamic-sized horizontal patches with facial images. Sci Rep. 12(1), 17297 (2022) https:\/\/doi.org\/10.1038\/s41598-022-21380-4","journal-title":"Sci Rep."},{"issue":"7","key":"1602_CR41","doi-asserted-by":"publisher","first-page":"1313","DOI":"10.3390\/healthcare10071313","volume":"10","author":"J Rasheed","year":"2022","unstructured":"Rasheed, J., Shubair, R.M.: Screening lung diseases using cascaded feature generation and selection strategies. Healthcare (Basel). 10(7), 1313 (2022) https:\/\/doi.org\/10.3390\/healthcare10071313","journal-title":"Healthcare (Basel)."},{"issue":"1","key":"1602_CR42","doi-asserted-by":"publisher","first-page":"6034971","DOI":"10.1155\/2022\/6034971","volume":"2022","author":"E Kaplan","year":"2022","unstructured":"Kaplan, E., Ekinci, T., Kaplan, S., Barua, P.D., Dogan, S., Tuncer, T., Tan, R.-S., Arunkumar, N., Acharya, U.R.: Pfp-lhcinca: Pyramidal fixed-size patch-based feature extraction and chi-square iterative neighborhood component analysis for automated fetal sex classification on ultrasound images. Contrast Media & Molecular Imaging. 2022(1), 6034971 (2022) https:\/\/doi.org\/10.1155\/2022\/6034971","journal-title":"Contrast Media & Molecular Imaging."},{"issue":"14","key":"1602_CR43","doi-asserted-by":"publisher","first-page":"42735","DOI":"10.1007\/s11042-023-16676-0","volume":"83","author":"B Tasci","year":"2024","unstructured":"Tasci, B., Tasci, G., Ayyildiz, H., Kamath, A.P., Barua, P.D., Tuncer, T., Dogan, S., Ciaccio, E.J., Chakraborty, S., Acharya, U.R.: Automated schizophrenia detection model using blood sample scattergram images and local binary pattern. Multimedia Tools Appl. 83(14), 42735\u201342763 (2024) https:\/\/doi.org\/10.1007\/s11042-023-16676-0","journal-title":"Multimedia Tools Appl."},{"issue":"1","key":"1602_CR44","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1186\/s40537-016-0043-6","volume":"3","author":"K Weiss","year":"2016","unstructured":"Weiss, K., Khoshgoftaar, T.M., Wang, D.: A survey of transfer learning. J Big Data. 3(1), 9 (2016) https:\/\/doi.org\/10.1186\/s40537-016-0043-6","journal-title":"J Big Data."},{"key":"1602_CR45","doi-asserted-by":"publisher","unstructured":"Tan, C., Sun, F., Kong, T., Zhang, W., Yang, C., Liu, C.: A survey on deep transfer learning. In: Artificial Neural Networks and Machine Learning\u2013ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270\u2013279 (2018). https:\/\/doi.org\/10.1007\/978-3-030-01424-7_27. Springer","DOI":"10.1007\/978-3-030-01424-7_27"},{"key":"1602_CR46","doi-asserted-by":"publisher","unstructured":"Kornblith, S., Shlens, J., Le, Q.V.: Do better imagenet models transfer better? In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2661\u20132671 (2019). https:\/\/doi.org\/10.48550\/arXiv.1805.08974","DOI":"10.48550\/arXiv.1805.08974"},{"key":"1602_CR47","doi-asserted-by":"publisher","unstructured":"Wang, Y., Sun, D., Chen, K., Lai, F., Chowdhury, M.: Egeria: Efficient dnn training with knowledge-guided layer freezing. In: Proceedings of the Eighteenth European Conference on Computer Systems, pp. 851\u2013866 (2023). https:\/\/doi.org\/10.1145\/3552326.3587451","DOI":"10.1145\/3552326.3587451"},{"key":"1602_CR48","doi-asserted-by":"publisher","unstructured":"Yang, L., Lin, S., Zhang, F., Zhang, J., Fan, D.: Efficient self-supervised continual learning with progressive task-correlated layer freezing. In: 2025 26th International Symposium on Quality Electronic Design (ISQED), pp. 1\u20138 (2025). https:\/\/doi.org\/10.1109\/ISQED65160.2025.11014440 . IEEE","DOI":"10.1109\/ISQED65160.2025.11014440"},{"key":"1602_CR49","doi-asserted-by":"publisher","unstructured":"Fr\u00e9gier, Y., Gouray, J.-B.: Mind2mind: transfer learning for GANs. In: Nielsen, F., Barbaresco, F. (eds.) Geometric Science of Information (GSI 2021). Lecture Notes in Computer Science, vol. 12829, pp. 851\u2013859. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-80209-7_91","DOI":"10.1007\/978-3-030-80209-7_91"},{"key":"1602_CR50","doi-asserted-by":"publisher","unstructured":"Davila, A., Colan, J., Hasegawa, Y.: Comparison of fine-tuning strategies for transfer learning in medical image classification. Image Vis Comput. 146, 105012 (2024) https:\/\/doi.org\/10.1016\/j.imavis.2024.105012","DOI":"10.1016\/j.imavis.2024.105012"},{"issue":"1","key":"1602_CR51","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1186\/s12880-022-00793-7","volume":"22","author":"HE Kim","year":"2022","unstructured":"Kim, H.E., Cosa-Linan, A., Santhanam, N., Jannesari, M., Maros, M.E., Ganslandt, T.: Transfer learning for medical image classification: a literature review. BMC Med Imaging. 22(1), 69 (2022) https:\/\/doi.org\/10.1186\/s12880-022-00793-7","journal-title":"BMC Med Imaging."},{"key":"1602_CR52","doi-asserted-by":"publisher","unstructured":"McManus, D.P., Gray, D.J., Zhang, W., Yang, Y.: Diagnosis, treatment, and management of echinococcosis. BMJ. 344, 3866 (2012) https:\/\/doi.org\/10.1136\/bmj.e3866","DOI":"10.1136\/bmj.e3866"},{"issue":"2","key":"1602_CR53","doi-asserted-by":"publisher","first-page":"103","DOI":"10.4103\/2229-5070.190812","volume":"6","author":"P Mehta","year":"2016","unstructured":"Mehta, P., Prakash, M., Khandelwal, N.: Radiological manifestations of hydatid disease and its complications. Tropical parasitology. 6(2), 103\u2013112 (2016)","journal-title":"Tropical parasitology."},{"issue":"10","key":"1602_CR54","doi-asserted-by":"publisher","first-page":"1880","DOI":"10.1371\/journal.pntd.0001880","volume":"6","author":"M Stojkovic","year":"2012","unstructured":"Stojkovic, M., Rosenberger, K., Kauczor, H.-U., Junghanss, T., Hosch, W.: Diagnosing and staging of cystic echinococcosis: how do CT and MRI perform in comparison to ultrasound? PLoS Negl Trop Dis. 6(10), 1880 (2012) https:\/\/doi.org\/10.1371\/journal.pntd.0001880","journal-title":"PLoS Negl Trop Dis."},{"issue":"1","key":"1602_CR55","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1038\/s41591-018-0300-7","volume":"25","author":"EJ Topol","year":"2019","unstructured":"Topol, E.J.: High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 25(1), 44\u201356 (2019) https:\/\/doi.org\/10.1038\/s41591-018-0300-7","journal-title":"Nat Med."},{"issue":"3","key":"1602_CR56","doi-asserted-by":"publisher","first-page":"2265","DOI":"10.1007\/s11760-023-02899-z","volume":"18","author":"Z Zhang","year":"2024","unstructured":"Zhang, Z., Jiang, S., Pan, X.: CTNet: rethinking convolutional neural networks and vision transformer for medical image segmentation. Signal Image Video Process. 18(3), 2265\u20132275 (2024) https:\/\/doi.org\/10.1007\/s11760-023-02899-z","journal-title":"Signal Image Video Process."},{"key":"1602_CR57","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3433374","author":"Q Gou","year":"2024","unstructured":"Gou, Q., Ren, Y.: Research on multi-scale cnn and transformer-based multi-level multi-classification method for images. IEEE Access. (2024) 1https:\/\/doi.org\/10.1109\/ACCESS.2024.3433374","journal-title":"IEEE Access."},{"issue":"6","key":"1602_CR58","doi-asserted-by":"publisher","first-page":"2888","DOI":"10.1109\/TVCG.2023.3261935","volume":"29","author":"Y Li","year":"2023","unstructured":"Li, Y., Wang, J., Dai, X., Wang, L., Yeh, C.-C.M., Zheng, Y., Zhang, W., Ma, K.-L.: How does attention work in vision transformers? a visual analytics attempt. IEEE Trans Vis Comput Graph. 29(6), 2888\u20132900 (2023) https:\/\/doi.org\/10.1109\/TVCG.2023.3261935","journal-title":"IEEE Trans Vis Comput Graph."},{"key":"1602_CR59","unstructured":"Ma, J., Bai, Y., Zhong, B., Zhang, W., Yao, T., Mei, T.: Visualizing and understanding patch interactions in vision transformer. arXiv preprint arXiv:2203.05922. (2022)"}],"container-title":["Journal of Imaging Informatics in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-025-01602-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-025-01602-7","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-025-01602-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T16:24:17Z","timestamp":1776875057000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-025-01602-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,8]]},"references-count":59,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,4]]}},"alternative-id":["1602"],"URL":"https:\/\/doi.org\/10.1007\/s10278-025-01602-7","relation":{},"ISSN":["2948-2933"],"issn-type":[{"value":"2948-2933","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,8]]},"assertion":[{"value":"8 March 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 June 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 June 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 July 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":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval"}},{"value":"Not applicable","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Participate"}},{"value":"Not applicable","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Publication"}},{"value":"The authors declare no competing interests.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}]}}