{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T02:47:25Z","timestamp":1782096445323,"version":"3.54.5"},"reference-count":59,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T00:00:00Z","timestamp":1782086400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T00:00:00Z","timestamp":1782086400000},"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 Med Syst"],"DOI":"10.1007\/s10916-026-02418-w","type":"journal-article","created":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T02:01:08Z","timestamp":1782093668000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MTA-Swin: A Multi-Token Attention Swin Transformer for Brain Tumor Classification with Leakage-Free MRI Benchmarking"],"prefix":"10.1007","volume":"50","author":[{"given":"Dong","family":"Lu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Divya","family":"Chaudhary","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,22]]},"reference":[{"key":"2418_CR1","doi-asserted-by":"publisher","unstructured":"Louis, D. N., Perry, A., Wesseling, P., Brat, D. J., Cree, I. A., Figarella-Branger, D., Hawkins, C., Ng, H.-K., Pfister, S. M., Reifenberger, G., Soffietti, R., Deimling, A., and Ellison, D. W., The 2021 who classification of tumors of the central nervous system: a summary. Neuro-Oncol. 23(8):1231\u20131251, 2021. https:\/\/doi.org\/10.1093\/neuonc\/noab106","DOI":"10.1093\/neuonc\/noab106"},{"key":"2418_CR2","doi-asserted-by":"publisher","unstructured":"Ostrom, Q. T., Cioffi, G., Waite, K., Kruchko, C., and Barnholtz-Sloan, J. S., Cbtrus statistical report: Primary brain and other central nervous system tumors diagnosed in the united states in 2014\u20132018. Neuro-Oncol. 23(Suppl 3):1\u2013105, 2021. https:\/\/doi.org\/10.1093\/neuonc\/noab200","DOI":"10.1093\/neuonc\/noab200"},{"key":"2418_CR3","doi-asserted-by":"publisher","unstructured":"Sung, H., Ferlay, J., Siegel, R. L., Laversanne, M., Soerjomataram, I., Jemal, A., and Bray, F., Global cancer statistics 2020: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer J. Clinic. 71(3):209\u2013249, 2021. https:\/\/doi.org\/10.3322\/caac.21660","DOI":"10.3322\/caac.21660"},{"key":"2418_CR4","doi-asserted-by":"publisher","unstructured":"Weller, M., Wick, W., Aldape, K., Brada, M., Berger, M., Pfister, S. M., Nishikawa, R., Rosenthal, M., Wen, P. Y., Stupp, R., and Reifenberger, G., Glioma. Nat. Rev. Dis. Primers 1:15017, 2015. https:\/\/doi.org\/10.1038\/nrdp.2015.17","DOI":"10.1038\/nrdp.2015.17"},{"key":"2418_CR5","doi-asserted-by":"publisher","unstructured":"Riemenschneider, M. J., Perry, A., and Reifenberger, G., Meningioma. Nat. Rev. Dis. Primers 7(1):75, 2021. https:\/\/doi.org\/10.1038\/s41572-021-00302-6","DOI":"10.1038\/s41572-021-00302-6"},{"key":"2418_CR6","doi-asserted-by":"publisher","unstructured":"Molitch, M. E., Diagnosis and treatment of pituitary adenomas: A review. JAMA 317(5):516\u2013524, 2017. https:\/\/doi.org\/10.1001\/jama.2016.19699","DOI":"10.1001\/jama.2016.19699"},{"key":"2418_CR7","doi-asserted-by":"publisher","unstructured":"Martucci, M., Muto, M., Russo, C., Altiero, M., Piccolo, C., and Cuocolo, R., Magnetic resonance imaging of primary adult brain tumors. Semin. Roentgenol. 58(3):173\u2013183, 2023. https:\/\/doi.org\/10.1053\/j.ro.2023.03.004","DOI":"10.1053\/j.ro.2023.03.004"},{"key":"2418_CR8","doi-asserted-by":"publisher","unstructured":"Kaifi, J. T., Malik, M. U., Mughal, M. A., Siddiqui, M. I., Ahmad, U., Shah, A., Sarwar, H., and Khan, M. Z., A review of recent advances in brain tumor diagnosis. Diagnostics 13(4):682, 2023. https:\/\/doi.org\/10.3390\/diagnostics13040682","DOI":"10.3390\/diagnostics13040682"},{"key":"2418_CR9","doi-asserted-by":"publisher","unstructured":"Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., Laak, J. A. W. M., Ginneken, B., and S\u00e1nchez, C. I., A survey on deep learning in medical image analysis. Med. Image Anal. 42:60\u201388, 2017. https:\/\/doi.org\/10.1016\/j.media.2017.07.005","DOI":"10.1016\/j.media.2017.07.005"},{"key":"2418_CR10","unstructured":"Nickparvar, M., Brain tumor MRI dataset. Kaggle. Accessed: Feb. 2025 (2021). https:\/\/www.kaggle.com\/datasets\/masoudnickparvar\/brain-tumor-mri-dataset"},{"key":"2418_CR11","doi-asserted-by":"publisher","unstructured":"LeCun, Y., Bengio, Y., and Hinton, G., Deep learning. Nature 521(7553):436\u2013444, 2015. https:\/\/doi.org\/10.1038\/nature14539","DOI":"10.1038\/nature14539"},{"key":"2418_CR12","doi-asserted-by":"publisher","unstructured":"Khan, M. A., Armaghan, M., Zaidi, S. A. A., Alqahtani, A., Alsubaie, N., Alsaif, H., Alduraibi, S., Alzahrani, N., and Alshahrani, B., Comparison of vision transformers and convolutional neural networks in medical image analysis: A systematic review. Diagnostics 13(6):1134, 2023. https:\/\/doi.org\/10.3390\/diagnostics13061134","DOI":"10.3390\/diagnostics13061134"},{"key":"2418_CR13","unstructured":"Raghu, M., Unterthiner, T., Kornblith, S., Zhang, C., and Dosovitskiy, A., Do vision transformers see like convolutional neural networks? In: Advances in Neural Information Processing Systems (NeurIPS), vol. 34, pp. 12116\u201312128, 2021. https:\/\/papers.nips.cc\/paper\/2021\/hash\/01a9019fbb89f49bb0bb0aa2e9f73c7b-Abstract.html"},{"key":"2418_CR14","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., and Houlsby, N., An image is worth 16\u00d716 words: Transformers for image recognition at scale, 2020. arXiv preprint arXiv:2010.11929"},{"key":"2418_CR15","doi-asserted-by":"publisher","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and 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"},{"key":"2418_CR16","unstructured":"Wang, F., Yu, Y., Wei, G., Shao, W., Zhou, Y., Yuille, A., and Xie, C., Scaling laws in patchification: An image is worth 50,176 tokens and more, 2025. arXiv preprint arXiv:2502.03738"},{"key":"2418_CR17","doi-asserted-by":"crossref","unstructured":"Yun, S., and Ro, Y., Shvit: Single-head vision transformer with memory efficient macro design. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5756\u20135767, 2024. https:\/\/doi.org\/10.1109\/CVPR52733.2024.00550 . Discussion of Swin Transformer on pp. 5758, 5765\u20135766. https:\/\/openaccess.thecvf.com\/content\/CVPR2024\/papers\/Yun_SHViT_Single-Head_Vision_Transformer_with_Memory_Efficient_Macro_Design_CVPR_2024_paper.pdf","DOI":"10.1109\/CVPR52733.2024.00550"},{"key":"2418_CR18","unstructured":"Golovneva, O., Wang, T., Weston, J., and Sukhbaatar, S., Multi-token attention, 2025. arXiv preprint arXiv:2504.00927"},{"key":"2418_CR19","doi-asserted-by":"publisher","unstructured":"Khushi, H. M. T., et\u00a0al., Performance analysis of state-of-the-art cnn architectures for brain tumour detection. Intern. J. Imag. Syst. Technol. 34(4):22949, 2024. https:\/\/doi.org\/10.1002\/ima.22949","DOI":"10.1002\/ima.22949"},{"key":"2418_CR20","doi-asserted-by":"publisher","unstructured":"Reyes, D., and S\u00e1nchez, J., Performance of convolutional neural networks for the classification of brain tumors using magnetic resonance imaging. Heliyon 10(3):25468, 2024. https:\/\/doi.org\/10.1016\/j.heliyon.2024.e25468","DOI":"10.1016\/j.heliyon.2024.e25468"},{"key":"2418_CR21","doi-asserted-by":"publisher","unstructured":"Elhadidy, M. S., et\u00a0al., Comparative analysis for accurate multi-classification of brain tumour based on significant deep learning models. Comput. Biol. Med., 2025. https:\/\/doi.org\/10.1016\/j.compbiomed.2025.109872","DOI":"10.1016\/j.compbiomed.2025.109872"},{"key":"2418_CR22","doi-asserted-by":"publisher","unstructured":"Mathivanan, S. K., Sonaimuthu, S., Murugesan, S., Rajadurai, H., Shivahare, B. D., and Shah, M. A., Employing deep learning and transfer learning for accurate brain tumor detection. Scientif Rep 14:7232, 2024. https:\/\/doi.org\/10.1038\/s41598-024-57970-7","DOI":"10.1038\/s41598-024-57970-7"},{"key":"2418_CR23","doi-asserted-by":"publisher","unstructured":"Bhat, J. I., and Rasool, N., Brain tumour detection using machine and deep learning: A systematic review. Multimed. Tools Appl., 2024. https:\/\/doi.org\/10.1007\/s11042-024-19333-2 . Early Access","DOI":"10.1007\/s11042-024-19333-2"},{"key":"2418_CR24","doi-asserted-by":"crossref","unstructured":"Hashemi, S. M. H., Safari, L., and Dadashzadeh\u00a0Taromi, A., Realism in action: Anomaly-aware diagnosis of brain tumors from medical images using yolov8 and deit, 2024. arXiv preprint arXiv:2401.03302","DOI":"10.1109\/ICCKE68588.2025.11273333"},{"key":"2418_CR25","doi-asserted-by":"publisher","unstructured":"Al\u00a0Bataineh, A. F., Nahar, K. M. O., Khafajeh, H., Samara, G., Alazaidah, R., Nasayreh, A., Bashkami, A., Gharaibeh, H., and Dawaghreh, W., Enhanced magnetic resonance imaging-based brain tumor classification with a hybrid swin transformer and resnet50v2 model. Appl. Sci. 14(22):10154, 2024. https:\/\/doi.org\/10.3390\/app142210154","DOI":"10.3390\/app142210154"},{"key":"2418_CR26","doi-asserted-by":"publisher","unstructured":"Nahiduzzaman, M., Abdulrazak, L. F., Kibria, H. B., Khandakar, A., Ayari, M. A., Ahamed, M. F., Ahsan, M., Haider, J., Moni, M. A., and Kowalski, M., A hybrid explainable model based on advanced machine learning and deep learning models for classifying brain tumors using mri images. Scientif. Rep. 15:1649, 2025. https:\/\/doi.org\/10.1038\/s41598-025-85874-7","DOI":"10.1038\/s41598-025-85874-7"},{"key":"2418_CR27","doi-asserted-by":"publisher","unstructured":"Panthakkan, A., Anzar, S. M., and Mansoor, W., Unleashing the power of efficientnet-convnext concatenation for brain tumor classification. In: Proceedings of the 15th Biomedical Engineering International Conference (BMEiCON 2023). IEEE, ???, 2023. https:\/\/doi.org\/10.1109\/BMEiCON60347.2023.10322070","DOI":"10.1109\/BMEiCON60347.2023.10322070"},{"key":"2418_CR28","doi-asserted-by":"publisher","unstructured":"Pacal, I., Celik, O., Bayram, B., and Cunha, A., Enhancing efficientnetv2 with global and efficient channel attention mechanisms for accurate mri-based brain tumor classification. Cluster Comput. 27(8):11187\u201311212, 2024. https:\/\/doi.org\/10.1007\/s10586-024-04532-1","DOI":"10.1007\/s10586-024-04532-1"},{"key":"2418_CR29","doi-asserted-by":"publisher","unstructured":"Noor, S., AlQahtani, S. A., and Khan, S., XGBoost-Liver: An intelligent integrated features approach for classifying liver diseases using ensemble XGBoost training model. Comput. Mater. Continua 83(1):1435\u20131450, 2025. https:\/\/doi.org\/10.32604\/cmc.2025.061700","DOI":"10.32604\/cmc.2025.061700"},{"key":"2418_CR30","doi-asserted-by":"publisher","unstructured":"Khan, S., Noor, S., Awan, H. H., Iqbal, S., AlQahtani, S. A., Dilshad, N., Ahmad, N., Deep-ProBind: Binding protein prediction with transformer-based deep learning model. BMC Bioinf. 26:88, 2025. https:\/\/doi.org\/10.1186\/s12859-025-06101-8","DOI":"10.1186\/s12859-025-06101-8"},{"key":"2418_CR31","doi-asserted-by":"publisher","unstructured":"Almusallam, N., Khan, S., Alarfaj, F. K., and Ahmad, N., A robust deep learning framework for RNA 5-methyluridine modification prediction using integrated features. BMC Biol. 23:328, 2025. https:\/\/doi.org\/10.1186\/s12915-025-02433-2","DOI":"10.1186\/s12915-025-02433-2"},{"key":"2418_CR32","doi-asserted-by":"publisher","unstructured":"Aljohani, M., Bahgat, W. M., Balaha, H. M., AbdulAzeem, Y., El-Abd, M., Badawy, M., and Elhosseini, M. A., An automated metaheuristic-optimized approach for diagnosing and classifying brain tumors based on a convolutional neural network. Results Eng. 23:102459, 2024. https:\/\/doi.org\/10.1016\/j.rineng.2024.102459","DOI":"10.1016\/j.rineng.2024.102459"},{"key":"2418_CR33","doi-asserted-by":"publisher","unstructured":"Tonmoy, M. R., Shams, M. A., and Adnan, M. A., X-brain: Explainable recognition of brain tumors using robust deep attention cnn. Biomed. Signal Process. Control 100:106988, 2025. https:\/\/doi.org\/10.1016\/j.bspc.2024.106988","DOI":"10.1016\/j.bspc.2024.106988"},{"key":"2418_CR34","doi-asserted-by":"crossref","unstructured":"Khan, M. A., and Auvee, R. B. Z., Comparative analysis of resource-efficient cnn architectures for brain tumor classification, 2024. arXiv preprint arXiv:2411.15596","DOI":"10.1109\/ICCIT64611.2024.11021970"},{"key":"2418_CR35","doi-asserted-by":"publisher","unstructured":"Mohanty, B. C., Subudhi, P. K., Dash, R., and Mohanty, B., Feature-enhanced deep learning technique with soft attention for mri-based brain tumor classification. Intern. J. Inf. Technol. 16:1617\u20131626, 2024. https:\/\/doi.org\/10.1007\/s41870-023-01701-0","DOI":"10.1007\/s41870-023-01701-0"},{"key":"2418_CR36","doi-asserted-by":"publisher","unstructured":"Wang, J., Lu, S., Wang, S. H., and Zhang, Y., Ranmerformer: Randomized vision transformer with token merging for brain tumor classification. Neurocomputing 573:127216, 2024. https:\/\/doi.org\/10.1016\/j.neucom.2023.127216","DOI":"10.1016\/j.neucom.2023.127216"},{"key":"2418_CR37","doi-asserted-by":"publisher","unstructured":"Khan, S., Dilshad, N., Ahmad, N., Noor, S., and AlQahtani, S. A., Integrating AI in security information and event management for real time cyber defense. Scientif. Rep. 15:35872, 2025. https:\/\/doi.org\/10.1038\/s41598-025-19689-x","DOI":"10.1038\/s41598-025-19689-x"},{"key":"2418_CR38","doi-asserted-by":"publisher","unstructured":"Paul, J. S., Plassard, A. J., Landman, B. A., and Fabbri, D., Deep learning for brain tumor classification. In: Madabhushi, A., Dowling, J., Chen, W. (Eds.), Medical Imaging 2017: Biomedical Applications in Molecular, Structural, and Functional Imaging, Vol. 10137, p. 1013710. SPIE, ???, 2017. https:\/\/doi.org\/10.1117\/12.2254195","DOI":"10.1117\/12.2254195"},{"key":"2418_CR39","doi-asserted-by":"publisher","unstructured":"Ari, A., and Hanbay, D., Deep learning based brain tumor classification and detection system. Turkish J. Electric. Eng. Comput. Sci. 26(5):2275\u20132286, 2018. https:\/\/doi.org\/10.3906\/elk-1702-125","DOI":"10.3906\/elk-1702-125"},{"key":"2418_CR40","doi-asserted-by":"publisher","unstructured":"Das, S., Aranya, O. R. R., and Labiba, N. N., Brain tumor classification using convolutional neural network. In: 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), pp. 1\u20135. IEEE, ???, 2019. https:\/\/doi.org\/10.1109\/ICASERT.2019.8934562","DOI":"10.1109\/ICASERT.2019.8934562"},{"key":"2418_CR41","doi-asserted-by":"publisher","unstructured":"Deepak, S., and Ameer, P. M., Brain tumor classification using deep cnn features via transfer learning. Comput. Biol. Med. 111:103345, 2019. https:\/\/doi.org\/10.1016\/j.compbiomed.2019.103345","DOI":"10.1016\/j.compbiomed.2019.103345"},{"key":"2418_CR42","doi-asserted-by":"publisher","unstructured":"Afshar, P., Plataniotis, K. N., and Mohammadi, A., Capsule networks for brain tumor classification based on mri images and coarse tumor boundaries. In: ICASSP 2019 - IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1368\u20131372. IEEE, Brighton, UK, 2019. https:\/\/doi.org\/10.1109\/ICASSP.2019.8683759","DOI":"10.1109\/ICASSP.2019.8683759"},{"key":"2418_CR43","doi-asserted-by":"publisher","unstructured":"Swati, Z. N. K., Zhao, Q., Kabir, M., Ali, F., Ali, Z., Ahmed, S., and Lu, J., Brain tumor classification for mr images using transfer learning and fine-tuning. Computer. Med. Imag. Graph. 75:34\u201346, 2019. https:\/\/doi.org\/10.1016\/j.compmedimag.2019.05.001","DOI":"10.1016\/j.compmedimag.2019.05.001"},{"key":"2418_CR44","doi-asserted-by":"publisher","unstructured":"Kang, J., Ullah, Z., and Gwak, J., Mri-based brain tumor classification using ensemble of deep features and machine learning classifiers. Sensors 21(6):2222, 2021. https:\/\/doi.org\/10.3390\/s21062222","DOI":"10.3390\/s21062222"},{"key":"2418_CR45","doi-asserted-by":"publisher","unstructured":"Sharif, M. I., Khan, M. A., Alhussein, M., Aurangzeb, K., and Raza, M., A decision support system for multimodal brain tumor classification using deep learning. Compl. Intell. Syst. 8(4):3007\u20133020, 2022. https:\/\/doi.org\/10.1007\/s40747-021-00321-0 . First online: 2021","DOI":"10.1007\/s40747-021-00321-0"},{"key":"2418_CR46","doi-asserted-by":"publisher","unstructured":"Sadad, T., Rehman, A., Munir, A., Saba, T., Tariq, U., Ayesha, N., and Abbasi, R., Brain tumor detection and multi-classification using advanced deep learning techniques. Microsc. Res. Techniq. 84(6):1296\u20131308, 2021. https:\/\/doi.org\/10.1002\/jemt.23688","DOI":"10.1002\/jemt.23688"},{"key":"2418_CR47","doi-asserted-by":"publisher","unstructured":"Raza, A., Ayub, H., Khan, J. A., et\u00a0al., A hybrid deep learning-based approach for brain tumor classification. Electronics 11(7):1146, 2022. https:\/\/doi.org\/10.3390\/electronics11071146","DOI":"10.3390\/electronics11071146"},{"key":"2418_CR48","doi-asserted-by":"publisher","unstructured":"Shorten, C., and Khoshgoftaar, T. M., A survey on image data augmentation for deep learning. J. Big Data 6(1):60, 2019. https:\/\/doi.org\/10.1186\/s40537-019-0197-0","DOI":"10.1186\/s40537-019-0197-0"},{"key":"2418_CR49","doi-asserted-by":"publisher","unstructured":"Wen, A., Zhong, A., Chen, Z., Hashimoto, D. A., Snyder, O., Silversmith, W., Mahmood, F., Huang, Y., Wang, F., Haber, J. M., et\u00a0al., Data leakage in medical imaging ai: an urgent call for transparency. Nat. Med. 26:1302\u20131303, 2020. https:\/\/doi.org\/10.1038\/s41591-020-1023-x","DOI":"10.1038\/s41591-020-1023-x"},{"key":"2418_CR50","doi-asserted-by":"publisher","unstructured":"Kapoor, S., and Narayanan, A., Leakage and the reproducibility crisis in ml-based science. Patterns 4(9):100804, 2023. https:\/\/doi.org\/10.1016\/j.patter.2023.100804","DOI":"10.1016\/j.patter.2023.100804"},{"key":"2418_CR51","unstructured":"Bhuvaji, S., Brain tumor classification (MRI), 2020. Kaggle. Accessed: Feb. 2025. https:\/\/www.kaggle.com\/datasets\/sartajbhuvaji\/brain-tumor-classification-mri\/data"},{"key":"2418_CR52","unstructured":"rm1000, Brain Tumor (MRI Scans), 2020. Kaggle. Accessed: Feb. 2025. https:\/\/www.kaggle.com\/datasets\/rm1000\/brain-tumor-mri-scans"},{"key":"2418_CR53","unstructured":"Hamada, A., Brain Tumor Detection (Br35h), 2021. Kaggle. Accessed: Feb. 2025. https:\/\/www.kaggle.com\/datasets\/ahmedhamada0\/brain-tumor-detection"},{"key":"2418_CR54","unstructured":"joeyz66, Cleaned Brain Tumour Dataset. Kaggle, 2023. Accessed: Feb. 2025. https:\/\/www.kaggle.com\/datasets\/joeyz66\/cleaned-brain-tumour-dataset"},{"key":"2418_CR55","doi-asserted-by":"publisher","unstructured":"Li, J., Chen, J., Tang, Y., Wang, C., Landman, B. A., and Zhou, S. K., Transforming medical imaging with transformers? a comparative review of key properties, current progresses, and future perspectives. Med. Image Anal. 85:102762, 2023. https:\/\/doi.org\/10.1016\/j.media.2023.102762","DOI":"10.1016\/j.media.2023.102762"},{"key":"2418_CR56","doi-asserted-by":"crossref","unstructured":"Wu, H., Xiao, B., Codella, N., Liu, M., Dai, X., Yuan, L., and Zhang, L., Cvt: Introducing convolutions to vision transformers. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 22\u201331, 2021. https:\/\/doi.org\/10.1109\/ICCV48922.2021.00009 . https:\/\/ieeexplore.ieee.org\/document\/9709880","DOI":"10.1109\/ICCV48922.2021.00009"},{"key":"2418_CR57","doi-asserted-by":"crossref","unstructured":"Yuan, L., Chen, Y., Wang, T., Yu, W., Shi, Y., Jiang, Z., Tay, F. E. H., Feng, J., and Yan, S., Incorporating convolution designs into visual transformers. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 559\u2013567, 2021. https:\/\/doi.org\/10.1109\/CVPR46437.2021.00062 . https:\/\/ieeexplore.ieee.org\/document\/9577313","DOI":"10.1109\/ICCV48922.2021.00062"},{"key":"2418_CR58","doi-asserted-by":"publisher","unstructured":"Cheng, J., Huang, W., Cao, S., Yang, R., Yang, W., Yun, Z., Wang, Z., and Feng, Q., Enhanced performance of brain tumor classification via tumor region augmentation and partition. PLoS ONE 10(10):0140381, 2015. https:\/\/doi.org\/10.1371\/journal.pone.0140381","DOI":"10.1371\/journal.pone.0140381"},{"key":"2418_CR59","doi-asserted-by":"crossref","unstructured":"Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H. R., and Xu, D., Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 574\u2013584, 2022. https:\/\/doi.org\/10.1109\/WACV51458.2022.00064 . https:\/\/ieeexplore.ieee.org\/document\/9706811","DOI":"10.1109\/WACV51458.2022.00181"}],"container-title":["Journal of Medical Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10916-026-02418-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10916-026-02418-w","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10916-026-02418-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T02:01:24Z","timestamp":1782093684000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10916-026-02418-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,22]]},"references-count":59,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["2418"],"URL":"https:\/\/doi.org\/10.1007\/s10916-026-02418-w","relation":{},"ISSN":["1573-689X"],"issn-type":[{"value":"1573-689X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,22]]},"assertion":[{"value":"2 October 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 May 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 June 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":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Clinical trial number"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Human Ethics and Consent to Participate declarations"}},{"value":"The authors received no specific funding for this work.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Funding declaration"}},{"value":"The authors declare no competing interests.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"100"}}