{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T06:46:32Z","timestamp":1791960392967,"version":"4.3.4"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T00:00:00Z","timestamp":1791849600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T00:00:00Z","timestamp":1791849600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001411","name":"Indian Council of Medical Research","doi-asserted-by":"publisher","award":["BMI\/014(03)\/TF\/2023"],"award-info":[{"award-number":["BMI\/014(03)\/TF\/2023"]}],"id":[{"id":"10.13039\/501100001411","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging. Inform. med."],"DOI":"10.1007\/s10278-026-02355-7","type":"journal-article","created":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T21:47:32Z","timestamp":1791928052000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Generalizability of Deep Learning Architectures for Oral Squamous Cell Carcinoma Classification in Histopathology: A Comparative CNN\u2013Vision Transformer Study with External Validation and Label-Quality Analysis"],"prefix":"10.1007","author":[{"given":"Beryl Rachel","family":"J.","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nivedhitha","family":"Maraimalai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anubhuti","family":"Sood","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yashdeep","family":"Singh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Trupti","family":"Jain","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Barath Raj","family":"K.","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Akshay","family":"Trimukhe","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vivek","family":"Nayyar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7624-1156","authenticated-orcid":false,"given":"Deepika","family":"Mishra","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Varun","family":"Surya","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rahul","family":"Yadav","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Harpreet","family":"Singh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kapil","family":"Sikka","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tanveer","family":"Ahmed","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,10,13]]},"reference":[{"key":"2355_CR1","first-page":"394","volume":"68","author":"F Bray","year":"2018","unstructured":"Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A: Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 68:394-424, 2018.","journal-title":"CA Cancer J Clin"},{"key":"2355_CR2","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1016\/j.oraloncology.2008.06.002","volume":"45","author":"S Warnakulasuriya","year":"2009","unstructured":"Warnakulasuriya S: Global epidemiology of oral and oropharyngeal cancer. Oral Oncol 45:309-316, 2009.","journal-title":"Oral Oncol"},{"key":"2355_CR3","first-page":"355","volume":"120","author":"F Rashid","year":"2015","unstructured":"Rashid F, Ahmad T, Khan MI, et al.: Inter-observer agreement and pitfalls in histopathological diagnosis of oral cancer. Oral Surg Oral Med Oral Pathol Oral Radiol 120:355-360, 2015.","journal-title":"Oral Surg Oral Med Oral Pathol Oral Radiol"},{"key":"2355_CR4","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","volume":"42","author":"G Litjens","year":"2017","unstructured":"Litjens G, Kooi T, Bejnordi BE, et al.: A survey on deep learning in medical image analysis. Med Image Anal 42:60-88, 2017.","journal-title":"Med Image Anal"},{"key":"2355_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101813","volume":"67","author":"CL Srinidhi","year":"2021","unstructured":"Srinidhi CL, Ciga O, Martel AL: Deep neural network models for computational histopathology: a survey. Med Image Anal 67:101813, 2021.","journal-title":"Med Image Anal"},{"key":"2355_CR6","doi-asserted-by":"publisher","unstructured":"Emegano DI, Mustapha MT, Ozsahin DU, Ozsahin I, Uzun B: Histopathology-based prostate cancer classification using ResNet: a comprehensive deep learning analysis. J Imaging Inform Med 39:604-619, 2026. https:\/\/doi.org\/10.1007\/s10278-025-01543-1, February 2026.","DOI":"10.1007\/s10278-025-01543-1"},{"issue":"868\u2013877","key":"2355_CR7","doi-asserted-by":"publisher","first-page":"2021","DOI":"10.1007\/s10120-021-01158-9","volume":"24","author":"Y Hu","year":"2021","unstructured":"Hu Y, Su F, Dong K, Wang X, Zhao X, Jiang Y, Li J, Ji J, Sun Y: Deep learning system for lymph node quantification and metastatic cancer identification from whole-slide pathology images. Gastric Cancer 24:868-877, 2021. https:\/\/doi.org\/10.1007\/s10120-021-01158-9","journal-title":"Gastric Cancer"},{"issue":"1507","key":"2355_CR8","doi-asserted-by":"publisher","first-page":"2025","DOI":"10.3390\/diagnostics15121507","volume":"15","author":"\u00d6A Solmaz","year":"2025","unstructured":"Solmaz \u00d6A, Tasci B: ViSwNeXtNet deep patch-wise ensemble of vision transformers and ConvNeXt for robust binary histopathology classification. Diagnostics (Basel) 15:1507, 2025. https:\/\/doi.org\/10.3390\/diagnostics15121507","journal-title":"Diagnostics (Basel)"},{"issue":"516","key":"2355_CR9","doi-asserted-by":"publisher","first-page":"2026","DOI":"10.1007\/s00521-026-12280-z","volume":"38","author":"N Gope","year":"2026","unstructured":"Gope N, Sakib N, Hosen MA: Histopathological image classification for enhanced diagnosis and prognosis through transfer learning and ensemble vision models. Neural Comput Appl 38:516, 2026. https:\/\/doi.org\/10.1007\/s00521-026-12280-z","journal-title":"Neural Comput Appl"},{"issue":"102078","key":"2355_CR10","doi-asserted-by":"publisher","first-page":"2021","DOI":"10.1016\/j.artmed.2021.102078","volume":"116","author":"D Karimi","year":"2021","unstructured":"Karimi D, Warfield SK, Gholipour A: Transfer learning in medical image segmentation: new insights from analysis of the dynamics of model parameters and learned representations. Artif Intell Med 116:102078, 2021. https:\/\/doi.org\/10.1016\/j.artmed.2021.102078","journal-title":"Artif Intell Med"},{"issue":"987","key":"2355_CR11","doi-asserted-by":"publisher","first-page":"2024","DOI":"10.3390\/cancers16050987","volume":"16","author":"B Song","year":"2024","unstructured":"Song B, Kc DR, Yang RY, Li S, Zhang C, Liang R: Classification of mobile-based oral cancer images using the vision transformer and the Swin transformer. Cancers (Basel) 16:987, 2024. https:\/\/doi.org\/10.3390\/cancers16050987","journal-title":"Cancers (Basel)"},{"key":"2355_CR12","doi-asserted-by":"publisher","first-page":"1387","DOI":"10.32604\/cmes.2022.018433","volume":"131","author":"W Alosaimi","year":"2022","unstructured":"Alosaimi W, Uddin MI: Efficient data augmentation techniques for improved classification in limited data set of oral squamous cell carcinoma. Comput Model Eng Sci 131:1387-1401, 2022. https:\/\/doi.org\/10.32604\/cmes.2022.018433.","journal-title":"Comput Model Eng Sci"},{"key":"2355_CR13","doi-asserted-by":"publisher","unstructured":"Fati SM, Senan EM, Javed Y: Early diagnosis of oral squamous cell carcinoma based on histopathological images using deep and hybrid learning approaches. Diagnostics (Basel) 12:1899, 2022. https:\/\/doi.org\/10.3390\/diagnostics12081899.","DOI":"10.3390\/diagnostics12081899"},{"key":"2355_CR14","doi-asserted-by":"publisher","first-page":"1321","DOI":"10.1177\/00220345221089858","volume":"101","author":"SY Yang","year":"2022","unstructured":"Yang SY, Li SH, Liu JL, et al.: Histopathology-based diagnosis of oral squamous cell carcinoma using deep learning. J Dent Res 101:1321-1327, 2022. https:\/\/doi.org\/10.1177\/00220345221089858.","journal-title":"J Dent Res"},{"key":"2355_CR15","doi-asserted-by":"publisher","first-page":"6364102","DOI":"10.1155\/2022\/6364102","volume":"2022","author":"MA Deif","year":"2022","unstructured":"Deif MA, Attar H, Amer A, Elhaty IA, Khosravi MR, Solyman AAA: Diagnosis of oral squamous cell carcinoma using deep neural networks and binary particle swarm optimization on histopathological images: an AIoMT approach. Comput Intell Neurosci 2022:6364102, 2022. https:\/\/doi.org\/10.1155\/2022\/6364102.","journal-title":"Comput Intell Neurosci"},{"key":"2355_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.104704","volume":"83","author":"HM Afify","year":"2023","unstructured":"Afify HM, Mohammed KK, Hassanien AE: Novel prediction model on OSCC histopathological images via deep transfer learning combined with Grad-CAM interpretation. Biomed Signal Process Control 83:104704, 2023. https:\/\/doi.org\/10.1016\/j.bspc.2023.104704.","journal-title":"Biomed Signal Process Control"},{"key":"2355_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.4018\/IJGHPC.320474","volume":"15","author":"SK Shetty","year":"2023","unstructured":"Shetty SK, Patil AP: Duck pack optimization with deep transfer learning-enabled oral squamous cell carcinoma classification on histopathological images. Int J Grid High Perform Comput 15:1-21, 2023. https:\/\/doi.org\/10.4018\/IJGHPC.320474.","journal-title":"Int J Grid High Perform Comput"},{"key":"2355_CR18","doi-asserted-by":"publisher","unstructured":"Ananthakrishnan B, Shaik A, Kumar S, Narendran SO, Mattu K, Kavitha MS: Automated detection and classification of oral squamous cell carcinoma using deep neural networks. Diagnostics (Basel) 13:918, 2023. https:\/\/doi.org\/10.3390\/diagnostics13050918.","DOI":"10.3390\/diagnostics13050918"},{"key":"2355_CR19","doi-asserted-by":"publisher","first-page":"2131","DOI":"10.3390\/ijerph20032131","volume":"20","author":"M Das","year":"2023","unstructured":"Das M, Dash R, Mishra SK: Automatic detection of oral squamous cell carcinoma from histopathological images of oral mucosa using deep convolutional neural network. Int J Environ Res Public Health 20:2131, 2023. https:\/\/doi.org\/10.3390\/ijerph20032131.","journal-title":"Int J Environ Res Public Health"},{"key":"2355_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2023.e13444","volume":"9","author":"S Panigrahi","year":"2023","unstructured":"Panigrahi S, Nanda BS, Bhuyan R, Kumar K, Ghosh S, Swarnkar T: Classifying histopathological images of oral squamous cell carcinoma using deep transfer learning. Heliyon 9:e13444, 2023. https:\/\/doi.org\/10.1016\/j.heliyon.2023.e13444.","journal-title":"Heliyon"},{"key":"2355_CR21","doi-asserted-by":"publisher","first-page":"601","DOI":"10.1186\/s12903-024-04307-5","volume":"24","author":"A Soni","year":"2024","unstructured":"Soni A, Sethy PK, Dewangan AK, Nanthaamornphong A, Behera SK, Devi B: Enhancing oral squamous cell carcinoma detection: a novel approach using improved EfficientNet architecture. BMC Oral Health 24:601, 2024.","journal-title":"BMC Oral Health"},{"key":"2355_CR22","doi-asserted-by":"publisher","first-page":"153303382513809","DOI":"10.1177\/15330338251380966","volume":"24","author":"M Wu","year":"2025","unstructured":"Wu M, Hu Y, Tian F, Lin H: EfficientNetSwift: a lightweight and precise deep learning model for detecting oral squamous cell carcinoma using pathological images. Technol Cancer Res Treat 24:15330338251380966, 2025. https:\/\/doi.org\/10.1177\/15330338251380966.","journal-title":"Technol Cancer Res Treat"},{"key":"2355_CR23","doi-asserted-by":"publisher","first-page":"1551876","DOI":"10.3389\/fonc.2025.1551876","volume":"15","author":"J Li","year":"2025","unstructured":"Li J: Fusion feature-based hybrid methods for diagnosing oral squamous cell carcinoma in histopathological images. Front Oncol 15:1551876, 2025. https:\/\/doi.org\/10.3389\/fonc.2025.1551876.","journal-title":"Front Oncol"},{"key":"2355_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2025.111609","volume":"159","author":"S Asif","year":"2025","unstructured":"Asif S, Wang VY, Xu D: OralTransNet: a novel hybrid model integrating transformer attention and CNN features for accurate diagnosis of mouth and oral diseases. Eng Appl Artif Intell 159:111609, 2025. https:\/\/doi.org\/10.1016\/j.engappai.2025.111609.","journal-title":"Eng Appl Artif Intell"},{"key":"2355_CR25","doi-asserted-by":"publisher","unstructured":"Pham TD: Integrating support vector machines and deep learning features for oral cancer histopathology analysis. Biol Methods Protoc 10:bpaf034, 2025. https:\/\/doi.org\/10.1093\/biomethods\/bpaf034.","DOI":"10.1093\/biomethods\/bpaf034"},{"key":"2355_CR26","doi-asserted-by":"publisher","DOI":"10.1007\/s10266-026-01448-7","author":"Y Balel","year":"2026","unstructured":"Balel Y, Sa\u011fta\u015f K, Teke F, Kurt MA: Deep learning-based automated detection of oral squamous cell carcinoma in histopathological images: a comparative study of five CNN architectures. Odontology, 2026. https:\/\/doi.org\/10.1007\/s10266-026-01448-7.","journal-title":"Odontology"},{"key":"2355_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.ibmed.2026.100387","volume":"14","author":"NR Manihira","year":"2026","unstructured":"Manihira NR, Sethy PK, Smerat A, Behera SK: Compact attention-driven deep network for interpretable oral squamous cell carcinoma screening. Intell Based Med 14:100387, 2026. https:\/\/doi.org\/10.1016\/j.ibmed.2026.100387.","journal-title":"Intell Based Med"},{"issue":"551\u2013566","key":"2355_CR28","doi-asserted-by":"publisher","first-page":"2024","DOI":"10.1111\/jop.13578","volume":"53","author":"Z Pirayesh","year":"2024","unstructured":"Pirayesh Z, Mohammad-Rahimi H, Ghasemi N, Motamedian SR, Sadeghi TS, Koohi H, Rokhshad R, Lotfi SM, Najafi A, Alajaji SA, Khoury ZH, Jessri M, Sultan AS: Deep learning-based image classification and segmentation on digital histopathology for oral squamous cell carcinoma: a systematic review and meta-analysis. J Oral Pathol Med 53:551-566, 2024. https:\/\/doi.org\/10.1111\/jop.13578","journal-title":"J Oral Pathol Med"},{"issue":"8803\u20138811","key":"2355_CR29","doi-asserted-by":"publisher","first-page":"2025","DOI":"10.1097\/MS9.0000000000004166","volume":"87","author":"MU Suleman","year":"2025","unstructured":"Suleman MU, Mursaleen M, Khalil U, Saboor A, Bilal M, Khan SA, Subhani MA, Hussnain MA, Tabassum SN, Tahir M: Assessing the generalizability of artificial intelligence in radiology: a systematic review of performance across different clinical settings. Ann Med Surg (Lond) 87:8803-8811, 2025. https:\/\/doi.org\/10.1097\/MS9.0000000000004166","journal-title":"Ann Med Surg (Lond)"},{"key":"2355_CR30","doi-asserted-by":"publisher","unstructured":"Sood A, Mishra D, Surya V, et al.: Establishing a national AI-ready imaging repository for oral tobacco-induced lesions: experience from Indian MIDAS-Oral Hub. Oral Dis 1\u20133, 2026. https:\/\/doi.org\/10.1111\/odi.70436.","DOI":"10.1111\/odi.70436"},{"key":"2355_CR31","doi-asserted-by":"crossref","unstructured":"Macenko M, Niethammer M, Marron JS, et al.: A method for normalizing histology slides for quantitative analysis. Proc IEEE Int Symp Biomed Imaging:1107\u20131110, 2009.","DOI":"10.1109\/ISBI.2009.5193250"},{"key":"2355_CR32","unstructured":"Kebede AF: Histopathological imaging dataset for oral cancer [dataset on the Internet]. Kaggle; 2024. Available at: https:\/\/www.kaggle.com\/datasets\/ashenafifasilkebede\/dataset. Accessed [insert access date]."},{"key":"2355_CR33","doi-asserted-by":"publisher","unstructured":"Rahman TY, Mahanta LB, Das AK, Sarma JD: Histopathological imaging database for oral cancer analysis . Mendeley Data V2, 2023. https:\/\/doi.org\/10.17632\/ftmp4cvtmb.2.","DOI":"10.17632\/ftmp4cvtmb.2"},{"key":"2355_CR34","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2022.894978","volume":"12","author":"Z Xu","year":"2022","unstructured":"Xu Z, Peng J, Zeng X, Xu H, Chen Q: High-accuracy oral squamous cell carcinoma auxiliary diagnosis system based on EfficientNet. Front Oncol 12:894978, 2022. https:\/\/doi.org\/10.3389\/fonc.2022.894978.","journal-title":"Front Oncol"},{"key":"2355_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.jobcr.2026.101478","volume":"16","author":"A Bharti","year":"2026","unstructured":"Bharti A, Kashyap N, Kamboj M, et al.: Comprehensive benchmarking of deep learning architectures for multiclass histopathological classification of oral epithelial lesions. J Oral Biol Craniofac Res 16:101478, 2026. https:\/\/doi.org\/10.1016\/j.jobcr.2026.101478.","journal-title":"J Oral Biol Craniofac Res"},{"key":"2355_CR36","doi-asserted-by":"publisher","first-page":"530","DOI":"10.1007\/s42979-026-05141-y","volume":"7","author":"M Nogueira","year":"2026","unstructured":"Nogueira M, Gomes EF: Data leakage concerns in training and evaluation protocols for oral cancer image classification. SN Comput Sci 7:530, 2026.","journal-title":"SN Comput Sci"},{"key":"2355_CR37","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.neunet.2020.05.003","volume":"128","author":"N Das","year":"2020","unstructured":"Das N, Hussain E, Mahanta LB: Automated classification of cells into multiple classes in epithelial tissue of oral squamous cell carcinoma using transfer learning and convolutional neural network. Neural Netw 128:47-60, 2020. https:\/\/doi.org\/10.1016\/j.neunet.2020.05.003.","journal-title":"Neural Netw"},{"key":"2355_CR38","doi-asserted-by":"publisher","first-page":"322","DOI":"10.1016\/j.jds.2022.08.017","volume":"18","author":"K Oya","year":"2023","unstructured":"Oya K, Kokomoto K, Nozaki K, Toyosawa S: Oral squamous cell carcinoma diagnosis in digitized histological images using convolutional neural network. J Dent Sci 18:322-329, 2023. https:\/\/doi.org\/10.1016\/j.jds.2022.08.017.","journal-title":"J Dent Sci"},{"key":"2355_CR39","doi-asserted-by":"publisher","first-page":"9915","DOI":"10.1007\/s00521-019-04516-y","volume":"32","author":"MM Fraz","year":"2020","unstructured":"Fraz MM, Khurram SA, Graham S, Shaban M, Hassan M, Loya A, et al.: FABnet: feature attention-based network for simultaneous segmentation of microvessels and nerves in routine histology images of oral cancer. Neural Comput Appl 32:9915-9928, 2020. https:\/\/doi.org\/10.1007\/s00521-019-04516-y.","journal-title":"Neural Comput Appl"},{"key":"2355_CR40","doi-asserted-by":"publisher","first-page":"8285","DOI":"10.3390\/app10228285","volume":"10","author":"F Martino","year":"2020","unstructured":"Martino F, Bloisi DD, Pennisi A, et al.: Deep learning-based pixel-wise lesion segmentation on oral squamous cell carcinoma images. Appl Sci 10:8285, 2020. https:\/\/doi.org\/10.3390\/app10228285.","journal-title":"Appl Sci"},{"issue":"e72938","key":"2355_CR41","doi-asserted-by":"publisher","first-page":"2025","DOI":"10.2196\/72938","volume":"13","author":"M Germaine","year":"2025","unstructured":"Germaine M, O'Higgins AC, Egan B, Healy G: Label accuracy in electronic health records and its impact on machine learning models for early prediction of gestational diabetes: 3-step retrospective validation study. JMIR Med Inform 13:e72938, 2025. https:\/\/doi.org\/10.2196\/72938","journal-title":"JMIR Med Inform"},{"issue":"103166","key":"2355_CR42","doi-asserted-by":"publisher","first-page":"2024","DOI":"10.1016\/j.media.2024.103166","volume":"95","author":"J Shi","year":"2024","unstructured":"Shi J, Zhang K, Guo C, Yang Y, Xu Y, Wu J: A survey of label-noise deep learning for medical image analysis. Med Image Anal 95:103166, 2024. https:\/\/doi.org\/10.1016\/j.media.2024.103166","journal-title":"Med Image Anal"},{"key":"2355_CR43","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2026.115472","volume":"181","author":"J Ramos","year":"2026","unstructured":"Ramos J, Pais-Vieira M, Br\u00e1s S: Small, imbalanced, and noisy datasets in healthcare research: a systematic review. Eng Appl Artif Intell 181:115472, 2026.","journal-title":"Eng Appl Artif Intell"},{"issue":"103422","key":"2355_CR44","doi-asserted-by":"publisher","first-page":"2025","DOI":"10.1016\/j.media.2024.103422","volume":"101","author":"J Zhu","year":"2025","unstructured":"Zhu J, Bolsterlee B, Song Y, Meijering E: Improving cross-domain generalizability of medical image segmentation using uncertainty and shape-aware continual test-time domain adaptation. Med Image Anal 101:103422, 2025. https:\/\/doi.org\/10.1016\/j.media.2024.103422","journal-title":"Med Image Anal"},{"key":"2355_CR45","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1007\/s10916-024-02105-8","volume":"48","author":"S Takahashi","year":"2024","unstructured":"Takahashi S, Sakaguchi Y, Kouno N, et al.: Comparison of vision transformers and convolutional neural networks in medical image analysis: a systematic review. J Med Syst 48:84, 2024. https:\/\/doi.org\/10.1007\/s10916-024-02105-8.","journal-title":"J Med Syst"},{"issue":"1","key":"2355_CR46","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1186\/s12880-025-02118-w","volume":"26","author":"F Ahmed","year":"2026","unstructured":"Ahmed, F., Naz, N. S., Khan, S., Rehman, A. U., Ismael, W. M., & Khan, M. A. (2026). Explainable artificial intelligence (XAI) in medical imaging: a systematic review of techniques, applications, and challenges. BMC medical imaging, 26(1), 37. https:\/\/doi.org\/10.1186\/s12880-025-02118-w","journal-title":"BMC medical imaging"},{"issue":"157","key":"2355_CR47","doi-asserted-by":"publisher","first-page":"2026","DOI":"10.3390\/bioengineering13020157","volume":"13","author":"K Pant","year":"2026","unstructured":"46. Pant K, Dutta Pramanik PK, Zhao Z: A robust ConvNeXt-based framework for efficient, generalizable, and explainable brain tumor classification on MRI. Bioengineering (Basel) 13:157, 2026. https:\/\/doi.org\/10.3390\/bioengineering13020157","journal-title":"Bioengineering (Basel)"},{"key":"2355_CR48","doi-asserted-by":"publisher","first-page":"780","DOI":"10.1186\/s12885-025-14193-x","volume":"25","author":"M Hadilou","year":"2025","unstructured":"47. Hadilou M, Mahdavi N, Keykha E, Ghofrani A, Tahmasebi E, Arabfard M: Artificial intelligence based vision transformer application for grading histopathological images of oral epithelial dysplasia: a step towards AI-driven diagnosis. BMC Cancer 25:780, 2025. https:\/\/doi.org\/10.1186\/s12885-025-14193-x.","journal-title":"BMC Cancer"}],"container-title":["Journal of Imaging Informatics in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-026-02355-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-026-02355-7","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-026-02355-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T06:03:26Z","timestamp":1791957806000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-026-02355-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10,13]]},"references-count":48,"alternative-id":["2355"],"URL":"https:\/\/doi.org\/10.1007\/s10278-026-02355-7","relation":{},"ISSN":["2948-2933"],"issn-type":[{"value":"2948-2933","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10,13]]},"assertion":[{"value":"29 June 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 September 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 September 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 October 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"This work was approved by Institutional Ethical Committee, All India Institute of Medical Sciences, New Delhi with IEC number AIIMSA4739\/01.10.2025 and IEC-984\/13.01.2023.","order":1,"name":"Ethics","label":"Ethics Approval","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Informed consent was obtained from all individual participants included in the study.","order":2,"name":"Ethics","label":"Consent to Participate","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The funding for this work was obtained by Dr. Deepika Mishra. None of the authors have conflict of interest.","order":3,"name":"Ethics","label":"Conflict of interest","group":{"name":"EthicsHeading","label":"Declarations"}}]}}