{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,3]],"date-time":"2026-11-03T20:04:46Z","timestamp":1793736286870,"version":"4.6.1"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,3,21]],"date-time":"2024-03-21T00:00:00Z","timestamp":1710979200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,3,21]],"date-time":"2024-03-21T00:00:00Z","timestamp":1710979200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100011755","name":"National Center for Research and Development","doi-asserted-by":"crossref","award":["grant no. POIR.01.01.01-00-0861\/16-00"],"award-info":[{"award-number":["grant no. POIR.01.01.01-00-0861\/16-00"]}],"id":[{"id":"10.13039\/501100011755","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Diagnostyka Consilio"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Cancer diseases constitute one of the most significant societal challenges. In this paper, we introduce a novel histopathological dataset for prostate cancer detection. The proposed dataset, consisting of over 2.6 million tissue patches extracted from 430 fully annotated scans, 4675 scans with assigned binary diagnoses, and 46 scans with diagnoses independently provided by a group of histopathologists can be found at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/michalkoziarski\/DiagSet\">https:\/\/github.com\/michalkoziarski\/DiagSet<\/jats:ext-link>\n                    . Furthermore, we propose a machine learning framework for detection of cancerous tissue regions and prediction of scan-level diagnosis, utilizing thresholding to abstain from the decision in uncertain cases. The proposed approach, composed of ensembles of deep neural networks operating on the histopathological scans at different scales, achieves 94.6% accuracy in patch-level recognition and is compared in a scan-level diagnosis with 9 human histopathologists showing high statistical agreement.\n                  <\/jats:p>","DOI":"10.1038\/s41598-024-52183-4","type":"journal-article","created":{"date-parts":[[2024,3,21]],"date-time":"2024-03-21T06:02:05Z","timestamp":1711000925000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":42,"title":["DiagSet: a dataset for prostate cancer histopathological image classification"],"prefix":"10.1038","volume":"14","author":[{"given":"Micha\u0142","family":"Koziarski","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bogus\u0142aw","family":"Cyganek","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Przemys\u0142aw","family":"Niedziela","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bogus\u0142aw","family":"Olborski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zbigniew","family":"Antosz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marcin","family":"\u017bydak","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bogdan","family":"Kwolek","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pawe\u0142","family":"W\u0105sowicz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrzej","family":"Buka\u0142a","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jakub","family":"Swad\u017aba","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Piotr","family":"Sitkowski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,3,21]]},"reference":[{"key":"52183_CR1","first-page":"4","volume":"68","author":"SM Albelda","year":"1993","unstructured":"Albelda, S. M. Role of integrins and other cell adhesion molecules in tumor progression and metastasis. Lab. Investig. 68, 4\u201317 (1993).","journal-title":"Lab. Investig."},{"key":"52183_CR2","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1002\/pros.10317","volume":"58","author":"JE K\u00f6nig","year":"2004","unstructured":"K\u00f6nig, J. E., Senge, T., Allhoff, E. P. & K\u00f6nig, W. Analysis of the inflammatory network in benign prostate hyperplasia and prostate cancer. Prostate 58, 121\u2013129 (2004).","journal-title":"Prostate"},{"key":"52183_CR3","first-page":"125","volume":"50","author":"DF Gleason","year":"1966","unstructured":"Gleason, D. F. Classification of prostatic carcinomas. Cancer Chemother. Rep. 50, 125\u2013128 (1966).","journal-title":"Cancer Chemother. Rep."},{"key":"52183_CR4","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1097\/PAS.0000000000000530","volume":"40","author":"JI Epstein","year":"2016","unstructured":"Epstein, J. I. et al. The 2014 International Society of Urological Pathology (ISUP) consensus conference on Gleason grading of prostatic carcinoma: Definition of grading patterns and proposal for a new grading system. Am. J. Surg. Pathol. 40, 244\u2013252 (2016).","journal-title":"Am. J. Surg. Pathol."},{"key":"52183_CR5","unstructured":"Krizhevsky, A., Sutskever, I. & Hinton, G.\u00a0E. ImageNet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems, 1097\u20131105 (2012)."},{"key":"52183_CR6","unstructured":"Simonyan, K. & Zisserman, A. Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556 (2014)."},{"key":"52183_CR7","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, 770\u2013778 (2016).","DOI":"10.1109\/CVPR.2016.90"},{"key":"52183_CR8","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J. & Wojna, Z. Rethinking the Inception architecture for computer vision. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2818\u20132826 (2016).","DOI":"10.1109\/CVPR.2016.308"},{"key":"52183_CR9","unstructured":"Dosovitskiy, A. et\u00a0al. An image is worth 16$$\\times$$16 words: Transformers for image recognition at scale. CoRR. arXiv:2010.11929 (2020)."},{"key":"52183_CR10","unstructured":"Srinidhi, C.\u00a0L., Ciga, O. & Martel, A.\u00a0L. Deep neural network models for computational histopathology: A survey. arXiv:1912.12378 (2019)."},{"key":"52183_CR11","doi-asserted-by":"crossref","unstructured":"Ra\u0327czkowski, \u0141., Mo\u017cejko, M., Zambonelli, J. & Szczurek, E. ARA: Accurate, reliable and active histopathological image classification framework with Bayesian deep learning. Sci. Rep. 9 (2019).","DOI":"10.1038\/s41598-019-50587-1"},{"key":"52183_CR12","unstructured":"Goodfellow, I., Bengio, Y. & Courville, A. Deep Learning. http:\/\/www.deeplearningbook.org (MIT Press, 2016)."},{"key":"52183_CR13","doi-asserted-by":"crossref","unstructured":"Liu, Z. et\u00a0al. Swin transformer: Hierarchical vision transformer using shifted windows. CoRR. arXiv:2103.14030 (2021).","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"52183_CR14","doi-asserted-by":"publisher","first-page":"1455","DOI":"10.1109\/TBME.2015.2496264","volume":"63","author":"FA Spanhol","year":"2016","unstructured":"Spanhol, F. A., Oliveira, L. S., Petitjean, C. & Heutte, L. A dataset for breast cancer histopathological image classification. IEEE Trans. Biomed. Eng. 63, 1455\u20131462. https:\/\/doi.org\/10.1109\/TBME.2015.2496264 (2016).","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"52183_CR15","doi-asserted-by":"publisher","unstructured":"Wan, T., Cao, J., Chen, J. & Qin, Z. Automated grading of breast cancer histopathology using cascaded ensemble with combination of multi-level image features. Neurocomputing 229, 34\u201344. https:\/\/doi.org\/10.1016\/j.neucom.2016.05.084 (2017).","DOI":"10.1016\/j.neucom.2016.05.084"},{"key":"52183_CR16","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1016\/j.neucom.2016.08.103","volume":"229","author":"X Pan","year":"2017","unstructured":"Pan, X. et al. Accurate segmentation of nuclei in pathological images via sparse reconstruction and deep convolutional networks. Neurocomputing 229, 88\u201399 (2017).","journal-title":"Neurocomputing"},{"key":"52183_CR17","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. et al. A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60\u201388. https:\/\/doi.org\/10.1016\/j.media.2017.07.005 (2017).","journal-title":"Med. Image Anal."},{"key":"52183_CR18","doi-asserted-by":"publisher","first-page":"29","DOI":"10.4103\/2153-3539.186902","volume":"7","author":"A Janowczyk","year":"2016","unstructured":"Janowczyk, A. & Madabhushi, A. Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases. J. Pathol. Inform. 7, 29 (2016).","journal-title":"J. Pathol. Inform."},{"key":"52183_CR19","doi-asserted-by":"publisher","first-page":"26286","DOI":"10.1038\/srep26286","volume":"6","author":"G Litjens","year":"2016","unstructured":"Litjens, G. et al. Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis. Sci. Rep. 6, 26286. https:\/\/doi.org\/10.1038\/srep26286 (2016).","journal-title":"Sci. Rep."},{"key":"52183_CR20","doi-asserted-by":"publisher","DOI":"10.1038\/srep45938","author":"M Vandenberghe","year":"2017","unstructured":"Vandenberghe, M. et al. Relevance of deep learning to facilitate the diagnosis of HER2 status in breast cancer. Sci. Rep.https:\/\/doi.org\/10.1038\/srep45938 (2017).","journal-title":"Sci. Rep."},{"key":"52183_CR21","doi-asserted-by":"publisher","first-page":"1228","DOI":"10.1097\/01.pas.0000173646.99337.b1","volume":"29","author":"JI Epstein","year":"2005","unstructured":"Epstein, J. I. et al. The 2005 International Society of Urological Pathology (ISUP) consensus conference on Gleason grading of prostatic carcinoma. Am. J. Surg. Pathol 29, 1228\u20131242 (2005).","journal-title":"Am. J. Surg. Pathol"},{"key":"52183_CR22","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1016\/s1470-2045(19)30739-9","volume":"21","author":"W Bulten","year":"2020","unstructured":"Bulten, W. et al. Automated deep-learning system for Gleason grading of prostate cancer using biopsies: A diagnostic study. Lancet Oncol. 21, 233\u2013241. https:\/\/doi.org\/10.1016\/s1470-2045(19)30739-9 (2020).","journal-title":"Lancet Oncol."},{"key":"52183_CR23","unstructured":"Campanella, G., Silva, V. W.\u00a0K. & Fuchs, T.\u00a0J. Terabyte-scale deep multiple instance learning for classification and localization in pathology. arXiv:1805.06983 (2018)."},{"key":"52183_CR24","doi-asserted-by":"publisher","first-page":"12054","DOI":"10.1038\/s41598-018-30535-1","volume":"8","author":"E Arvaniti","year":"2018","unstructured":"Arvaniti, E. et al. Automated Gleason grading of prostate cancer tissue microarrays via deep learning. Sci. Rep. 8, 12054. https:\/\/doi.org\/10.1038\/s41598-018-30535-1 (2018).","journal-title":"Sci. Rep."},{"key":"52183_CR25","unstructured":"Bulten, W. & Litjens, G. Unsupervised prostate cancer detection on H &E using convolutional adversarial autoencoders. arXiv:1804.07098 (2018)."},{"key":"52183_CR26","doi-asserted-by":"crossref","unstructured":"Ren, J., Hacihaliloglu, I., Singer, E.\u00a0A., Foran, D.\u00a0J. & Qi, X. Adversarial domain adaptation for classification of prostate histopathology whole-slide images. arXiv:1806.01357 (2018).","DOI":"10.3389\/fbioe.2019.00102"},{"key":"52183_CR27","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1038\/s41746-019-0112-2","volume":"2","author":"K Nagpal","year":"2019","unstructured":"Nagpal, K. et al. Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer. npj Digit. Med. 2, 48. https:\/\/doi.org\/10.1038\/s41746-019-0112-2 (2019).","journal-title":"npj Digit. Med."},{"key":"52183_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2018.09.005","author":"G Nir","year":"2018","unstructured":"Nir, G. et al. Automatic grading of prostate cancer in digitized histopathology images: Learning from multiple experts. Med. Image Anal.https:\/\/doi.org\/10.1016\/j.media.2018.09.005 (2018).","journal-title":"Med. Image Anal."},{"key":"52183_CR29","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1038\/s41591-021-01620-2","volume":"28","author":"W Bulten","year":"2022","unstructured":"Bulten, W. et al. Artificial intelligence for diagnosis and Gleason grading of prostate cancer: The panda challenge. Nat. Med. 28, 154\u2013163. https:\/\/doi.org\/10.1038\/s41591-021-01620-2 (2022).","journal-title":"Nat. Med."},{"key":"52183_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2020.105637","author":"J Silva-Rodr\u00edguez","year":"2020","unstructured":"Silva-Rodr\u00edguez, J., Colomer, A., Sales, M. A., Molina, R. & Naranjo, V. Going deeper through the Gleason scoring scale: An automatic end-to-end system for histology prostate grading and cribriform pattern detection. Comput. Methods Programs Biomed.https:\/\/doi.org\/10.1016\/j.cmpb.2020.105637 (2020).","journal-title":"Comput. Methods Programs Biomed."},{"key":"52183_CR31","unstructured":"Automated Gleason Grading Challenge (2022). https:\/\/aggc22.grand-challenge.org\/AGGC22\/. Accessed Dec 2023."},{"key":"52183_CR32","doi-asserted-by":"crossref","unstructured":"Kim, J., Kwon\u00a0Lee, J. & Mu\u00a0Lee, K. Accurate image super-resolution using very deep convolutional networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1646\u20131654 (2016).","DOI":"10.1109\/CVPR.2016.182"},{"key":"52183_CR33","unstructured":"Otsu method. https:\/\/en.wikipedia.org\/wiki\/Otsu\u2019s_method (2023). Accessed Dec 2023."},{"key":"52183_CR34","doi-asserted-by":"crossref","unstructured":"Deng, J. et\u00a0al. ImageNet: A large-scale hierarchical image database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition, 248\u2013255 (IEEE, 2009).","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"52183_CR35","doi-asserted-by":"crossref","unstructured":"Xu, Z., Liu, R., Yang, S., Chai, Z. & Yuan, C. Learning imbalanced data with vision transformers. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2023).","DOI":"10.1109\/CVPR52729.2023.01516"},{"key":"52183_CR36","doi-asserted-by":"publisher","first-page":"16252","DOI":"10.1609\/aaai.v37i13.26986","volume":"37","author":"K Li","year":"2023","unstructured":"Li, K., Duggal, R. & Chau, D. H. Evaluating robustness of vision transformers on imbalanced datasets (student abstract). Proc. AAAI Conf. Artif. Intell. 37, 16252\u201316253. https:\/\/doi.org\/10.1609\/aaai.v37i13.26986 (2023).","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"52183_CR37","volume-title":"Handbook of Parametric and Nonparametric Statistical Procedures","author":"DJ Sheskin","year":"2007","unstructured":"Sheskin, D. J. Handbook of Parametric and Nonparametric Statistical Procedures 4th edn. (Chapman & Hall\/CRC, 2007).","edition":"4"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-52183-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-52183-4","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-52183-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,21]],"date-time":"2024-03-21T06:11:03Z","timestamp":1711001463000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-52183-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,21]]},"references-count":37,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["52183"],"URL":"https:\/\/doi.org\/10.1038\/s41598-024-52183-4","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,21]]},"assertion":[{"value":"16 March 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 January 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 March 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests:"}}],"article-number":"6780"}}