{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T07:29:24Z","timestamp":1784014164218,"version":"3.55.0"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2025,6,25]],"date-time":"2025-06-25T00:00:00Z","timestamp":1750809600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,6,25]],"date-time":"2025-06-25T00:00:00Z","timestamp":1750809600000},"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\/s10278-025-01582-8","type":"journal-article","created":{"date-parts":[[2025,6,25]],"date-time":"2025-06-25T13:50:15Z","timestamp":1750859415000},"page":"1651-1665","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Risk Classification of Low-Resolution Whole-Slide Thumbnail Images by Multi-dimensional Feature Reconstruction with Multi-task Deep Learning Network Helps Prioritize Pathology Case Registration"],"prefix":"10.1007","volume":"39","author":[{"given":"Cher-Wei","family":"Liang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu-Chen","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu-Yin","family":"Hsu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pei-Wei","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guan-Lin","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7553-6326","authenticated-orcid":false,"given":"Chiao-Min","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,6,25]]},"reference":[{"key":"1582_CR1","doi-asserted-by":"publisher","unstructured":"Santos MK, Ferreira JR, Wada DT, Ten\u00f3rio AP, Barbosa MHN, Marques PMA. Artificial intelligence, machine learning, computer-aided diagnosis, and radiomics: advances in imaging towards precision medicine. Radiol Bras. 2019;52(6):387\u2013396. https:\/\/doi.org\/10.1590\/0100-3984.2019.0049","DOI":"10.1590\/0100-3984.2019.0049"},{"key":"1582_CR2","doi-asserted-by":"publisher","unstructured":"Griem J, Ghaffar M, Wolf A, et al. Artificial intelligence-based tool for tumor detection and quantitative tissue analysis in colorectal specimens. Mod Pathol. 2023;36(12):100327. https:\/\/doi.org\/10.1016\/j.modpat.2023.100327","DOI":"10.1016\/j.modpat.2023.100327"},{"key":"1582_CR3","doi-asserted-by":"publisher","unstructured":"Janowczyk A, Madabhushi A. Deep learning for digital pathology image analysis: a comprehensive tutorial with selected use cases. J Pathol Inform. 2016;7:29. https:\/\/doi.org\/10.4103\/2153-3539.186902","DOI":"10.4103\/2153-3539.186902"},{"key":"1582_CR4","unstructured":"Ahmad WSHMW, Al-Bahadili H, Mahmood QS, et al. Classification of nasopharyngeal cases using DenseNet deep learning architecture. arXiv pre-print arXiv:2404.03188; 2024."},{"key":"1582_CR5","doi-asserted-by":"publisher","unstructured":"Wodzinski M, Marini N, Atzori M, M\u00fcller H. RegWSI: whole-slide image registration using combined deep feature- and intensity-based methods: winner of the ACROBAT 2023 challenge. Comput Methods Programs Biomed. 2024;250:108187. https:\/\/doi.org\/10.1016\/j.cmpb.2024.108187","DOI":"10.1016\/j.cmpb.2024.108187"},{"key":"1582_CR6","doi-asserted-by":"crossref","unstructured":"Ding R, Luong KD, Rodriguez E, et al. Combining graph neural network and Mamba to capture local and global tissue spatial relationships in whole-slide images. arXiv pre-print arXiv:2406.04377; 2024.","DOI":"10.1038\/s41598-025-99042-4"},{"key":"1582_CR7","doi-asserted-by":"crossref","unstructured":"Zhao C, Ghanem B. Thumbnet: one thumbnail image contains all you need for recognition. In: Proceedings of the 28th ACM International Conference on Multimedia (MM 2020). New York, NY : Association for Computing Machinery; 2020:1506\u20131514.","DOI":"10.1145\/3394171.3413937"},{"issue":"5","key":"1582_CR8","doi-asserted-by":"publisher","first-page":"524","DOI":"10.3390\/diagnostics14050524","volume":"14","author":"N Dimitriou","year":"2024","unstructured":"Dimitriou N, Arandjelovi\u0107 O, Harrison DJ. Magnifying networks for histopathological images with billions of pixels. Diagnostics. 2024;14(5):524. https:\/\/doi.org\/10.3390\/diagnostics14050524","journal-title":"Diagnostics"},{"key":"1582_CR9","doi-asserted-by":"publisher","first-page":"106496","DOI":"10.1016\/j.compbiomed.2022.106496","volume":"153","author":"Y Zhao","year":"2023","unstructured":"Zhao Y, Wang X, Che T, Bao G, Li S. Multi-task deep learning for medical image computing and analysis: a review. Comput Biol Med. 2023;153:106496. https:\/\/doi.org\/10.1016\/j.compbiomed.2023.106496","journal-title":"Computers in Biology and Medicine."},{"issue":"4","key":"1582_CR10","doi-asserted-by":"publisher","first-page":"686","DOI":"10.1038\/s41416-020-01122-x","volume":"124","author":"A Echle","year":"2021","unstructured":"Echle A, Rindtorff NT, Brinker TJ, et al. Deep learning in cancer pathology: a new generation of clinical biomarkers. Br J Cancer. 2021;124(4):686-696. https:\/\/doi.org\/10.1038\/s41416-020-01122-x","journal-title":"Br J Cancer."},{"issue":"10","key":"1582_CR11","doi-asserted-by":"publisher","first-page":"1684","DOI":"10.1016\/j.ajpath.2020.10.018","volume":"191","author":"JY Cheng","year":"2021","unstructured":"Cheng JY, Abel JT, Balis UGJ, McClintock DS, Pantanowitz L. Challenges in the development, deployment, and regulation of artificial intelligence in anatomic pathology. Am J Pathol. 2021;191(10):1684\u20131692. https:\/\/doi.org\/10.1016\/j.ajpath.2020.10.018","journal-title":"Am J Pathol."},{"key":"1582_CR12","doi-asserted-by":"publisher","first-page":"104129","DOI":"10.1016\/j.compbiomed.2020.104129","volume":"128","author":"M Salvi","year":"2021","unstructured":"Salvi M, Acharya UR, Molinari F, Meiburger KM. The impact of pre- and post-image processing techniques on deep learning frameworks: a comprehensive review for digital pathology image analysis. Comput Biol Med. 2021;128:104129. https:\/\/doi.org\/10.1016\/j.compbiomed.2020.104129","journal-title":"Comput Biol Med."},{"key":"1582_CR13","doi-asserted-by":"publisher","first-page":"101813","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. 2021;67:101813. https:\/\/doi.org\/10.1016\/j.media.2020.101813","journal-title":"Medical image analysis."},{"key":"1582_CR14","unstructured":"Gedraite ES, Hadad M. Investigation on the effect of a gaussian blur in image filtering and segmentation. In: 53rd International Symposium ELMAR (ELMAR 2011). Piscataway, NJ : Institute of Electrical and Electronics Engineers; 2011:393\u2013396."},{"issue":"1","key":"1582_CR15","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","volume":"9","author":"N Otsu","year":"1979","unstructured":"Otsu N. A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern. 1979;9(1):62-66. https:\/\/doi.org\/10.1109\/TSMC.1979.4310076","journal-title":"IEEE Transactions on Systems Man and Cybernetics."},{"key":"1582_CR16","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation. In: Navab N, Hornegger J, Wells W, Frangi A, eds. Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015. Lecture Notes in Computer Science, vol 9351. Cham : Springer International Publishing; 2015:234-241.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"1582_CR17","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016). Piscataway, NJ : IEEE Computer Society; 2016:770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"1582_CR18","doi-asserted-by":"crossref","unstructured":"Hu J, Shen L, Sun G. Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2018). Piscataway, NJ : IEEE Computer Society; 2018:7132\u20137141.","DOI":"10.1109\/CVPR.2018.00745"},{"issue":"1","key":"1582_CR19","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/0031-3203(95)00067-4","volume":"29","author":"T Ojala","year":"1996","unstructured":"Ojala T, Pietik\u00e4inen M, Harwood D. A comparative study of texture measures with classification based on feature distributions. Pattern Recognit. 1996;29(1):51\u201359.","journal-title":"Pattern recognition."},{"issue":"2","key":"1582_CR20","first-page":"31","volume":"1","author":"G Othman","year":"2020","unstructured":"Othman G, Zeebaree DQ. The applications of discrete wavelet transform in image processing: a review. J Soft Comput Data Min. 2020;1(2):31\u201343.","journal-title":"Journal of Soft Computing and Data Mining."},{"issue":"3","key":"1582_CR21","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1186\/1746-1596-3-17","volume":"18","author":"K Kayser","year":"2008","unstructured":"Kayser K, G\u00f6rtler J, Goldmann T, Vollmer E, Hufnagl P, Kayser G. Image standards in tissue-based diagnosis (diagnostic surgical pathology). Diagn Pathol. 2008;3:17.","journal-title":"Diagn Pathol."},{"issue":"4","key":"1582_CR22","doi-asserted-by":"publisher","first-page":"21","DOI":"10.4103\/2153-3539.116866","volume":"22","author":"TL Sellaro","year":"2013","unstructured":"Sellaro TL, Husain M, Schmidt RL, et al. Relationship between magnification and resolution in digital pathology systems. J Pathol Inform. 2013;4:21.","journal-title":"J Pathol Inform."},{"key":"1582_CR23","doi-asserted-by":"crossref","unstructured":"Jadon S. A survey of loss functions for semantic segmentation. In: Proceedings of the 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB 2020). Piscataway, NJ : Institute of Electrical and Electronics Engineers; 2020:1\u20137.","DOI":"10.1109\/CIBCB48159.2020.9277638"},{"key":"1582_CR24","unstructured":"Zhang Z, Sabuncu MR. Generalized cross-entropy loss for training deep neural networks with noisy labels. In: Advances in Neural Information Processing Systems 31 (NeurIPS 2018). Red Hook, NY : Curran Associates, Inc.; 2018:8792\u20138802."},{"issue":"1","key":"1582_CR25","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1006\/jmps.1999.1279","volume":"44","author":"MW Browne","year":"2000","unstructured":"Browne MW. Cross-validation methods. J Math Psychol. 2000;44(1):108\u2013132.","journal-title":"Journal of mathematical psychology."},{"issue":"2","key":"1582_CR26","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1016\/S1076-6332(03)00671-8","volume":"11","author":"KH Zou","year":"2004","unstructured":"Zou KH, Warfield SK, Fendrick AM, et al. Statistical validation of image segmentation quality based on a spatial overlap index. Acad Radiol. 2004;11(2):178\u2013189.","journal-title":"Academic radiology."},{"key":"1582_CR27","doi-asserted-by":"crossref","unstructured":"Rezatofighi H, Tsoi N, Gwak J, Sadeghian A, Reid I, Savarese S. Generalized intersection over union: a metric and a loss for bounding box regression. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2019). Piscataway, NJ : IEEE Computer Society; 2019:658\u2013666.","DOI":"10.1109\/CVPR.2019.00075"},{"issue":"9","key":"1582_CR28","doi-asserted-by":"publisher","first-page":"850","DOI":"10.1109\/34.232073","volume":"15","author":"DP Huttenlocher","year":"1993","unstructured":"Huttenlocher DP, Klanderman GA, Rucklidge WJ. Comparing images using the Hausdorff distance. IEEE Trans Pattern Anal Mach Intell. 1993;15(9):850\u2013863.","journal-title":"IEEE Transactions on pattern analysis and machine intelligence."},{"key":"1582_CR29","first-page":"1","volume":"74","author":"MQP Smith","year":"2020","unstructured":"Smith MQP, Ruxton GD. Effective use of the McNemar test. Behav Ecol Sociobiol. 2020;74:1\u20139.","journal-title":"Behavioral Ecology and Sociobiology"},{"key":"1582_CR30","doi-asserted-by":"crossref","unstructured":"Yu W, Zhou P, Yan S, Wang X. InceptionNext: when Inception meets ConvNeXt. arXiv pre-print arXiv:2303.16900; 2023.","DOI":"10.1109\/CVPR52733.2024.00542"},{"key":"1582_CR31","doi-asserted-by":"crossref","unstructured":"Roy S, Koehler G, Ulrich C, et al. Mednext: transformer-driven scaling of convnets for medical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2023. Lecture Notes in Computer Science, vol 14223. Cham : Springer International Publishing; 2023:405\u2013415.","DOI":"10.1007\/978-3-031-43901-8_39"},{"key":"1582_CR32","unstructured":"Chen J, Lu MY, Shen Y, et al. TransUNet: transformers make strong encoders for medical image segmentation. arXiv pre-print arXiv:2102.04306; 2021."},{"key":"1582_CR33","doi-asserted-by":"crossref","unstructured":"He Y, Nath V, Yang D, Tang Y, Myronenko A, Xu D. Swinunetr-v2: stronger swin transformers with stagewise convolutions for 3d medical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2023. Lecture Notes in Computer Science, vol 14223. Cham : Springer International Publishing; 2023:416\u2013426.","DOI":"10.1007\/978-3-031-43901-8_40"},{"key":"1582_CR34","unstructured":"Tan M, Le Q. Efficientnetv2: smaller models and faster training. In: Proceedings of the 38th International Conference on Machine Learning (ICML 2021). Cambridge, MA : PMLR; 2021:10096\u201310106."},{"key":"1582_CR35","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, et al. An image is worth 16\u00d716 words: transformers for image recognition at scale. arXiv pre-print arXiv:2010.11929; 2020."},{"key":"1582_CR36","doi-asserted-by":"crossref","unstructured":"Liu Z, Hu H, Lin Y, et al. Swin transformer v2: scaling up capacity and resolution. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022). Piscataway, NJ : IEEE Computer Society; 2022:12009\u201312019.","DOI":"10.1109\/CVPR52688.2022.01170"},{"issue":"4","key":"1582_CR37","doi-asserted-by":"publisher","first-page":"1034","DOI":"10.1007\/s10278-020-00351-z","volume":"33","author":"N Kumar","year":"2020","unstructured":"Kumar N, Gupta R, Gupta S. Whole slide imaging in pathology: current perspectives and future directions. J Digit Imaging. 2020;33(4):1034\u20131040.","journal-title":"J Digit Imaging."},{"key":"1582_CR38","doi-asserted-by":"crossref","unstructured":"Xu K, Qin M, Sun F, Wang Y, Chen YK, Ren F. Learning in the frequency domain. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2020). Piscataway, NJ : IEEE Computer Society; 2020:1740\u20131749.","DOI":"10.1109\/CVPR42600.2020.00181"},{"key":"1582_CR39","doi-asserted-by":"crossref","unstructured":"Poli M, Ribeiro MP, Bomman M, et al. Transform once: efficient operator learning in frequency domain. Adv Neural Inf Process Syst. 2022;35:7947\u20137959.","DOI":"10.52202\/068431-0577"},{"key":"1582_CR40","doi-asserted-by":"crossref","unstructured":"Zhu X, Cheng D, Zhang Z, Lin S, Dai J. An empirical study of spatial attention mechanisms in deep networks. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV 2019). Piscataway, NJ : IEEE Computer Society; 2019:6688\u20136697.","DOI":"10.1109\/ICCV.2019.00679"},{"key":"1582_CR41","unstructured":"Zhang X, Chen Y, Wei M, et al. RFAConv: innovating spatial attention and standard convolutional operation. arXiv pre-print arXiv:2304.03198; 2023."}],"container-title":["Journal of Imaging Informatics in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-025-01582-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-025-01582-8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-025-01582-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T16:24:36Z","timestamp":1776875076000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-025-01582-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,25]]},"references-count":41,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,4]]}},"alternative-id":["1582"],"URL":"https:\/\/doi.org\/10.1007\/s10278-025-01582-8","relation":{},"ISSN":["2948-2933"],"issn-type":[{"value":"2948-2933","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,25]]},"assertion":[{"value":"26 February 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 May 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 June 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 June 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The study was approved by the Institutional Review Board of the Fu Jen Catholic University Hospital (IRB No.: FJUH111233).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}]}}