{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T00:31:42Z","timestamp":1776213102695,"version":"3.50.1"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62076096"],"award-info":[{"award-number":["62076096"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shanghai Knowledge Service Platform Project","award":["ZF1213"],"award-info":[{"award-number":["ZF1213"]}]},{"name":"the Fundamental Research Funds for the Central Universities"},{"name":"STCSM Project","award":["22ZR1421700"],"award-info":[{"award-number":["22ZR1421700"]}]},{"name":"Foundation of Key Laboratory of System Control and Information Processing,  Ministry of Education"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2025,4]]},"DOI":"10.1007\/s13042-024-02391-1","type":"journal-article","created":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T13:02:15Z","timestamp":1727787735000},"page":"2285-2296","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Multi-view representation for pathological image classification via contrastive learning"],"prefix":"10.1007","volume":"16","author":[{"given":"Kaitao","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shiliang","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Feng","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingjiu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,10,1]]},"reference":[{"issue":"2","key":"2391_CR1","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1038\/s41379-021-00929-0","volume":"35","author":"MG Hanna","year":"2022","unstructured":"Hanna MG, Ardon O, Reuter VE, Sirintrapun SJ, England C, Klimstra DS, Hameed MR (2022) Integrating digital pathology into clinical practice. Mod Pathol 35(2):152\u2013164","journal-title":"Mod Pathol"},{"issue":"5","key":"2391_CR2","doi-asserted-by":"publisher","first-page":"1225","DOI":"10.3390\/diagnostics12051225","volume":"12","author":"K Rakovic","year":"2022","unstructured":"Rakovic K, Colling R, Browning L, Dolton M, Horton MR, Protheroe A, Lamb AD, Bryant RJ, Scheffer R, Crofts J et al (2022) The use of digital pathology and artificial intelligence in histopathological diagnostic assessment of prostate cancer: A survey of prostate cancer uk supporters. Diagnostics 12(5):1225","journal-title":"Diagnostics"},{"key":"2391_CR3","doi-asserted-by":"publisher","first-page":"110674","DOI":"10.1109\/ACCESS.2019.2934486","volume":"7","author":"X Pan","year":"2019","unstructured":"Pan X, Li L, Yang D, He Y, Liu Z, Yang H (2019) An accurate nuclei segmentation algorithm in pathological image based on deep semantic network. IEEE Access 7:110674\u2013110686","journal-title":"IEEE Access"},{"key":"2391_CR4","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1016\/j.ejca.2021.10.007","volume":"160","author":"L Schneider","year":"2022","unstructured":"Schneider L, Laiouar-Pedari S, Kuntz S, Krieghoff-Henning E, Hekler A, Kather JN, Gaiser T, Fr\u00f6hling S, Brinker TJ (2022) Integration of deep learning-based image analysis and genomic data in cancer pathology: a systematic review. Eur J Cancer 160:80\u201391","journal-title":"Eur J Cancer"},{"issue":"8","key":"2391_CR5","doi-asserted-by":"publisher","first-page":"1301","DOI":"10.1038\/s41591-019-0508-1","volume":"25","author":"G Campanella","year":"2019","unstructured":"Campanella G, Hanna MG, Geneslaw L, Miraflor A, Werneck Krauss Silva V, Busam KJ, Brogi E, Reuter VE, Klimstra DS, Fuchs TJ (2019) Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nat Med 25(8):1301\u20131309","journal-title":"Nat Med"},{"issue":"6","key":"2391_CR6","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1038\/s41551-020-00682-w","volume":"5","author":"MY Lu","year":"2021","unstructured":"Lu MY, Williamson DF, Chen TY, Chen RJ, Barbieri M, Mahmood F (2021) Data-efficient and weakly supervised computational pathology on whole-slide images. Nat Biomed Eng 5(6):555\u2013570","journal-title":"Nat Biomed Eng"},{"issue":"9","key":"2391_CR7","doi-asserted-by":"publisher","first-page":"2307","DOI":"10.1038\/s41591-023-02504-3","volume":"29","author":"Z Huang","year":"2023","unstructured":"Huang Z, Bianchi F, Yuksekgonul M, Montine TJ, Zou J (2023) A visual-language foundation model for pathology image analysis using medical twitter. Nat Med 29(9):2307\u20132316","journal-title":"Nat Med"},{"issue":"2","key":"2391_CR8","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1038\/s41379-021-00929-0","volume":"35","author":"MG Hanna","year":"2022","unstructured":"Hanna MG, Ardon O, Reuter VE, Sirintrapun SJ, England C, Klimstra DS, Hameed MR (2022) Integrating digital pathology into clinical practice. Mod Pathol 35(2):152\u2013164","journal-title":"Mod Pathol"},{"issue":"1","key":"2391_CR9","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1109\/TMI.2020.3027547","volume":"40","author":"Y Liu","year":"2020","unstructured":"Liu Y, Yin M, Sun S (2020) Detexnet: accurately diagnosing frequent and challenging pediatric malignant tumors. IEEE Trans Med Imaging 40(1):395\u2013404","journal-title":"IEEE Trans Med Imaging"},{"key":"2391_CR10","doi-asserted-by":"publisher","first-page":"16577","DOI":"10.1109\/ACCESS.2022.3149637","volume":"10","author":"TAM Elhassan","year":"2022","unstructured":"Elhassan TAM, Rahim MSM, Swee TT, Hashim SZM, Aljurf M (2022) Feature extraction of white blood cells using CMYK-moment localization and deep learning in acute myeloid leukemia blood smear microscopic images. IEEE Access 10:16577\u201316591","journal-title":"IEEE Access"},{"key":"2391_CR11","doi-asserted-by":"crossref","unstructured":"Liu Y, Yin M, Sun S (2018) Multi-view learning and deep learning for microscopic neuroblastoma pathology image diagnosis. In: Pacific rim international conference on artificial intelligence. Springer, Nanjing, China, pp 545\u2013558","DOI":"10.1007\/978-3-319-97304-3_42"},{"issue":"8","key":"2391_CR12","doi-asserted-by":"publisher","first-page":"1049","DOI":"10.1049\/iet-cvi.2018.5349","volume":"12","author":"M Arya","year":"2018","unstructured":"Arya M, Mittal N, Singh G (2018) Texture-based feature extraction of smear images for the detection of cervical cancer. IET Comput Vis 12(8):1049\u20131059","journal-title":"IET Comput Vis"},{"issue":"5","key":"2391_CR13","first-page":"1014","volume":"41","author":"D Das","year":"2019","unstructured":"Das D, Mahanta LB, Ahmed S, Baishya BK, Haque I (2019) Automated classification of childhood brain tumours based on texture feature. Songklanakarin J Sci Technol 41(5):1014-1020","journal-title":"Songklanakarin J Sci Technol"},{"key":"2391_CR14","doi-asserted-by":"crossref","unstructured":"Jayachandran S, Ghosh A (2020) Deep transfer learning for texture classification in colorectal cancer histology. In: Artificial neural networks in pattern recognition. Springer, Winterthur, Switzerland, pp 173\u2013186","DOI":"10.1007\/978-3-030-58309-5_14"},{"issue":"9","key":"2391_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.celrep.2022.110424","volume":"38","author":"D Komura","year":"2022","unstructured":"Komura D, Kawabe A, Fukuta K, Sano K, Umezaki T, Koda H, Suzuki R, Tominaga K, Ochi M, Konishi H et al (2022) Universal encoding of pan-cancer histology by deep texture representations. Cell Rep 38(9):110424","journal-title":"Cell Rep"},{"issue":"7","key":"2391_CR16","doi-asserted-by":"publisher","first-page":"1455","DOI":"10.1109\/TBME.2015.2496264","volume":"63","author":"FA Spanhol","year":"2015","unstructured":"Spanhol FA, Oliveira LS, Petitjean C, Heutte L (2015) A dataset for breast cancer histopathological image classification. IEEE Trans Biomed Eng 63(7):1455\u20131462","journal-title":"IEEE Trans Biomed Eng"},{"issue":"1","key":"2391_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/srep27988","volume":"6","author":"JN Kather","year":"2016","unstructured":"Kather JN, Weis C-A, Bianconi F, Melchers SM, Schad LR, Gaiser T, Marx A, Z\u00f6llner FG (2016) Multi-class texture analysis in colorectal cancer histology. Sci Rep 6(1):1\u201311","journal-title":"Sci Rep"},{"key":"2391_CR18","unstructured":"Ilse M, Tomczak J, Welling M (2018) Attention-based deep multiple instance learning. In: International conference on machine learning. ACM, Stockholm, Sweden, pp 2127\u20132136"},{"issue":"6","key":"2391_CR19","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1038\/s41551-020-00682-w","volume":"5","author":"MY Lu","year":"2021","unstructured":"Lu MY, Williamson DF, Chen TY, Chen RJ, Barbieri M, Mahmood F (2021) Data-efficient and weakly supervised computational pathology on whole-slide images. Nat Biomed Eng 5(6):555\u2013570","journal-title":"Nat Biomed Eng"},{"key":"2391_CR20","first-page":"2136","volume":"34","author":"Z Shao","year":"2021","unstructured":"Shao Z, Bian H, Chen Y, Wang Y, Zhang J, Ji X et al (2021) Transmil: transformer based correlated multiple instance learning for whole slide image classification. Adv Neural Inf Process Syst 34:2136\u20132147","journal-title":"Adv Neural Inf Process Syst"},{"key":"2391_CR21","unstructured":"Sun Y, Huang X, Wang Y, Zhou H, Zhang Q (2021) Magnification-independent histopathological image classification with similarity-based multi-scale embeddings. arXiv preprint arXiv:2107.01063"},{"key":"2391_CR22","doi-asserted-by":"publisher","first-page":"2031","DOI":"10.1007\/s00521-013-1362-6","volume":"23","author":"S Sun","year":"2013","unstructured":"Sun S (2013) A survey of multi-view machine learning. Neural Comput Appl 23:2031\u20132038","journal-title":"Neural Comput Appl"},{"key":"2391_CR23","unstructured":"Wang W, Arora R, Livescu K, Bilmes J (2015) On deep multi-view representation learning. In: International conference on machine learning. ACM, Lille, France, pp 1083\u20131092"},{"key":"2391_CR24","doi-asserted-by":"crossref","unstructured":"Hotelling H (1992) Relations between two sets of variates. In: Samuel K, Norman L (eds) Johnson breakthroughs in statistics: methodology and distribution. Springer Verlag","DOI":"10.1007\/978-1-4612-4380-9_14"},{"issue":"6","key":"2391_CR25","doi-asserted-by":"publisher","first-page":"7412","DOI":"10.1109\/TPAMI.2022.3218605","volume":"45","author":"X Jia","year":"2022","unstructured":"Jia X, Jing X-Y, Sun Q, Chen S, Du B, Zhang D (2022) Human collective intelligence inspired multi-view representation learning-enabling view communication by simulating human communication mechanism. IEEE Trans Pattern Anal Mach Intell 45(6):7412\u20137429","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2391_CR26","doi-asserted-by":"crossref","unstructured":"Gao T, Yao X, Chen D (2021) Simcse: simple contrastive learning of sentence embeddings. arXiv preprint arXiv:2104.08821","DOI":"10.18653\/v1\/2021.emnlp-main.552"},{"key":"2391_CR27","first-page":"3988","volume":"35","author":"X Zhang","year":"2022","unstructured":"Zhang X, Zhao Z, Tsiligkaridis T, Zitnik M (2022) Self-supervised contrastive pre-training for time series via time-frequency consistency. Adv Neural Inf Process Syst 35:3988\u20134003","journal-title":"Adv Neural Inf Process Syst"},{"key":"2391_CR28","unstructured":"Chen T, Kornblith S, Norouzi M, Hinton G (2020) A simple framework for contrastive learning of visual representations. In: International conference on machine learning, Virtual Event. ACM, pp 1597\u20131607"},{"key":"2391_CR29","doi-asserted-by":"crossref","unstructured":"He K, Fan H, Wu Y, Xie S, Girshick R (2020) Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. IEEE, Seattle, WA, USA, pp 9729\u20139738","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"2391_CR30","unstructured":"Koch G, Zemel R, Salakhutdinov R, et al (2015) Siamese neural networks for one-shot image recognition. In: International conference on machine learning deep learning workshop. ACM, Lille, France, pp 1\u20138"},{"key":"2391_CR31","unstructured":"Robinson J, Chuang C-Y, Sra S, Jegelka S (2020) Contrastive learning with hard negative samples. arXiv preprint arXiv:2010.04592"},{"key":"2391_CR32","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. IEEE, Las Vegas, NV, USA,   pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"2391_CR33","unstructured":"Goyal P, Doll\u00e1r P, Girshick R, Noordhuis P, Wesolowski L, Kyrola A, Tulloch A, Jia Y, He K (2017) Accurate, large minibatch sgd: training imagenet in 1 hour. arXiv preprint arXiv:1706.02677"},{"key":"2391_CR34","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Van Der\u00a0Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. IEEE, Honolulu, HI, USA,   pp 4700\u20134708","DOI":"10.1109\/CVPR.2017.243"},{"key":"2391_CR35","doi-asserted-by":"crossref","unstructured":"Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L (2009) Imagenet: a large-scale hierarchical image database. In: IEEE conference on computer vision and pattern recognition. IEEE, Miami Beach, FL, USA, pp 248\u2013255","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"2391_CR36","doi-asserted-by":"crossref","unstructured":"Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, IEEE, Salt Lake City, UT, USA, pp 7132\u20137141","DOI":"10.1109\/CVPR.2018.00745"},{"key":"2391_CR37","unstructured":"Park J, Woo S, Lee J-Y, Kweon IS (2018) Bam: bottleneck attention module. arXiv preprint arXiv:1807.06514"},{"key":"2391_CR38","doi-asserted-by":"crossref","unstructured":"Woo S, Park J, Lee J-Y, Kweon IS (2018) Cbam: convolutional block attention module. In: Proceedings of the European conference on computer vision. Springer, Munich, Germany, pp 3\u201319","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"2391_CR39","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S et al (2020) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929"},{"key":"2391_CR40","doi-asserted-by":"crossref","unstructured":"Tian Y, Krishnan D, Isola P (2020) Contrastive multiview coding. In: Proceedings of the European conference on computer vision. Springer, Glasgow, UK, pp 776\u2013794","DOI":"10.1007\/978-3-030-58621-8_45"},{"key":"2391_CR41","unstructured":"Sowrirajan H, Yang J, Ng AY, Rajpurkar P (2021) Moco pretraining improves representation and transferability of chest X-ray models. In: Medical imaging with deep learning. PMLR, L\u00fcbeck, Germany, pp 728\u2013744"},{"key":"2391_CR42","doi-asserted-by":"crossref","unstructured":"Manna S, Bhattacharya S, Pal U (2021) Interpretive self-supervised pre-training: boosting performance on visual medical data. In: Proceedings of the twelfth Indian conference on computer vision, graphics and image processing. ACM, Jodhpur, India, pp 1\u20139","DOI":"10.1145\/3490035.3490273"},{"key":"2391_CR43","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2023.101859","volume":"98","author":"Y Zhang","year":"2023","unstructured":"Zhang Y, Deng L, Zhu H, Wang W, Ren Z, Zhou Q, Lu S, Sun S, Zhu Z, Gorriz JM et al (2023) Deep learning in food category recognition. Inf Fusion 98:101859","journal-title":"Inf Fusion"},{"key":"2391_CR44","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107567","volume":"109","author":"S-Y Lu","year":"2021","unstructured":"Lu S-Y, Nayak DR, Wang S-H, Zhang Y-D (2021) A cerebral microbleed diagnosis method via featurenet and ensembled randomized neural networks. Appl Soft Comput 109:107567","journal-title":"Appl Soft Comput"},{"key":"2391_CR45","doi-asserted-by":"publisher","first-page":"10799","DOI":"10.1007\/s00521-020-05082-4","volume":"33","author":"S Lu","year":"2021","unstructured":"Lu S, Wang S-H, Zhang Y-D (2021) Detection of abnormal brain in MRI via improved alexnet and elm optimized by chaotic bat algorithm. Neural Comput Appl 33:10799\u201310811","journal-title":"Neural Comput Appl"},{"issue":"2","key":"2391_CR46","doi-asserted-by":"publisher","first-page":"1572","DOI":"10.1002\/int.22686","volume":"37","author":"S Lu","year":"2022","unstructured":"Lu S, Zhu Z, Gorriz JM, Wang S-H, Zhang Y-D (2022) NAGNN: classification of COVID-19 based on neighboring aware representation from deep graph neural network. Int J Intell Syst 37(2):1572\u20131598","journal-title":"Int J Intell Syst"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02391-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-024-02391-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02391-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,7]],"date-time":"2025-04-07T07:29:08Z","timestamp":1744010948000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-024-02391-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,1]]},"references-count":46,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,4]]}},"alternative-id":["2391"],"URL":"https:\/\/doi.org\/10.1007\/s13042-024-02391-1","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,1]]},"assertion":[{"value":"16 October 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 September 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 October 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}