{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T10:20:47Z","timestamp":1777890047985,"version":"3.51.4"},"reference-count":38,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2024,9,19]],"date-time":"2024-09-19T00:00:00Z","timestamp":1726704000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Web Intelligence"],"published-print":{"date-parts":[[2025,8]]},"abstract":"<jats:p>With the rapid advancement of deep learning technologies, self-supervised learning utilizing large-scale unlabeled datasets has emerged as a dominant learning paradigm across multiple fields. This paradigm aligns well with the nature of medical imaging data, which has led to significant research efforts in applying self-supervised learning methods to this domain. However, many of these approaches fail to fully consider the unique characteristics of medical imaging, particularly the critical role that texture information plays in the diagnosis of thorax diseases. To address this gap, we propose a novel texture-aware self-supervised learning framework that leverages the Gray-Level Co-occurrence Matrix (GLCM) as an auxiliary signal to strengthen the model\u2019s capacity to extract disease-relevant texture features. Additionally, we integrate curriculum learning into our approach, which gradually emphasizes texture information throughout the training process. This method enables the model to more effectively capture the inherent characteristics of medical imaging data. Our qualitative and quantitative experimental results show that our approach surpasses the current state-of-the-art methods on both the NIH CXR and Stanford CheXpert datasets.<\/jats:p>","DOI":"10.3233\/web-240279","type":"journal-article","created":{"date-parts":[[2024,9,20]],"date-time":"2024-09-20T10:37:16Z","timestamp":1726828636000},"page":"338-349","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Harnessing the wisdom of a radiologist: Texture-aware curriculum self-supervised learning for thorax disease classification"],"prefix":"10.1177","volume":"23","author":[{"given":"Ningkang","family":"Peng","sequence":"first","affiliation":[{"name":"School of Computer and Electronic Information, Nanjing Normal University, Nanjing, Jiangsu, China."}]},{"given":"Shengjie","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Computer and Electronic Information, Nanjing Normal University, Nanjing, Jiangsu, China."}]},{"given":"Shuai","family":"Yuan","sequence":"additional","affiliation":[{"name":"School of Computer and Electronic Information, Nanjing Normal University, Nanjing, Jiangsu, China."}]},{"given":"Masaru","family":"Kitsuregawa","sequence":"additional","affiliation":[{"name":"Institute of Industrial Science, The University of Tokyo, Tokyo, Japan."}]},{"given":"Yanhui","family":"Gu","sequence":"additional","affiliation":[{"name":"School of Computer and Electronic Information, Nanjing Normal University, Nanjing, Jiangsu, China."}]}],"member":"179","published-online":{"date-parts":[[2024,9,19]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2916849"},{"key":"e_1_3_3_3_2","unstructured":"Bao H. Dong L. Wei F. Beit: BERT pre-training of image transformers 2021 CoRR arXiv:2106.08254."},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553380"},{"key":"e_1_3_3_5_2","series-title":"Proceedings of Machine Learning Research","first-page":"1597","volume-title":"Proceedings of the 37th International Conference on Machine Learning, ICML 2020","author":"Chen T.","year":"2020","unstructured":"Chen T., Kornblith S., Norouzi M., Hinton G.E., A simple framework for contrastive learning of visual representations, in: Proceedings of the 37th International Conference on Machine Learning, ICML 2020, Virtual Event, 13\u201318 July 2020, Proceedings of Machine Learning Research, Vol. 119, 2020, pp. 1597\u20131607, http:\/\/proceedings.mlr.press\/v119\/chen20j.html."},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01549"},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/n19-1423"},{"key":"e_1_3_3_8_2","unstructured":"Geirhos R. Rubisch P. Michaelis C. Bethge M. Wichmann F.A. Brendel W. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness in: 7th International Conference on Learning Representations ICLR 2019 New Orleans LA USA May 6\u20139 2019 2019 https:\/\/openreview.net\/forum?id=Bygh9j09KX."},{"key":"e_1_3_3_9_2","unstructured":"Grill J. Strub F. Altch\u00e9 F. Tallec C. Richemond P.H. Buchatskaya E. Doersch C. Pires B.\u00c1. Guo Z. Azar M.G. Piot B. Kavukcuoglu K. Munos R. Valko M. Bootstrap your own latent \u2013 a new approach to self-supervised learning in: Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020 NeurIPS 2020 Virtual December 6\u201312 2020 Larochelle H. Ranzato M. Hadsell R. Balcan M. Lin H. eds 2020. https:\/\/proceedings.neurips.cc\/paper\/2020\/hash\/f3ada80d5c4ee70142b17b8192b2958e-Abstract.html."},{"key":"e_1_3_3_10_2","doi-asserted-by":"publisher","unstructured":"Guo H. Somayajula S.A. Hosseini R. Xie P. Improving image classification of gastrointestinal endoscopy using curriculum self-supervised learning Scientific Reports 14(1) (2024) 6100. doi:10.1038\/s41598-024-53955-8.","DOI":"10.1038\/s41598-024-53955-8"},{"key":"e_1_3_3_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.02016"},{"key":"e_1_3_3_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.1973.4309314"},{"key":"e_1_3_3_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01553"},{"key":"e_1_3_3_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"e_1_3_3_15_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301590"},{"key":"e_1_3_3_16_2","doi-asserted-by":"publisher","DOI":"10.1145\/2647868.2654918"},{"key":"e_1_3_3_17_2","unstructured":"Kang M. Lu Y. Yuille A.L. Zhou Z. Data assemble: Leveraging multiple datasets with heterogeneous and partial labels 2021 CoRR arXiv:2109.12265."},{"key":"e_1_3_3_18_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01546"},{"key":"e_1_3_3_19_2","unstructured":"Kingma D.P. Welling M. Auto-encoding variational Bayes in: 2nd International Conference on Learning Representations ICLR 2014 Banff AB Canada April 14\u201316 2014 Conference Track Proceedings Bengio Y. LeCun Y. eds 2014. http:\/\/arxiv.org\/abs\/1312.6114."},{"key":"e_1_3_3_20_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.acl-short.64"},{"key":"e_1_3_3_21_2","doi-asserted-by":"publisher","unstructured":"Li X. Wei M. Chen S. Pointsmile: Point self-supervised learning via curriculum mutual information 2023 CoRR arXiv:2301.12744. doi:10.48550\/arXiv.","DOI":"10.48550\/arXiv"},{"key":"e_1_3_3_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3090866"},{"key":"e_1_3_3_23_2","unstructured":"Oord A. Li Y. Vinyals O. Representation learning with contrastive predictive coding 2018 CoRR arXiv:1807.03748."},{"key":"e_1_3_3_24_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-019-56882-1"},{"key":"e_1_3_3_25_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.03.127"},{"key":"e_1_3_3_26_2","doi-asserted-by":"publisher","unstructured":"Pouget E. Dedieu V. Applying self-supervised learning to image quality assessment in chest ct imaging Bioengineering 11(4) (2024) 335. doi:10.3390\/bioengineering11040335.","DOI":"10.3390\/bioengineering11040335"},{"key":"e_1_3_3_27_2","unstructured":"Rajpurkar P. Irvin J. Zhu K. Yang B. Mehta H. Duan T. Ding D.Y. Bagul A. Langlotz C.P. Shpanskaya K.S. Lungren M.P. Ng A.Y. Chexnet: Radiologist-level pneumonia detection on chest X-rays with deep learning 2017 CoRR arXiv:1711.05225."},{"key":"e_1_3_3_28_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.74"},{"key":"e_1_3_3_29_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-87722-4_1"},{"key":"e_1_3_3_30_2","series-title":"Proceedings of Machine Learning Research","first-page":"535","volume-title":"International Conference on Medical Imaging with Deep Learning, MIDL 2022","author":"Taher M.R.H.","year":"2022","unstructured":"Taher M.R.H., Haghighi F., Gotway M.B., Liang J., Caid: Context-aware instance discrimination for self-supervised learning in medical imaging, in: International Conference on Medical Imaging with Deep Learning, MIDL 2022, 6\u20138 July 2022, Konukoglu E., Menze B.H., Venkataraman A., Baumgartner C.F., Dou Q., Albarqouni S., eds, Proceedings of Machine Learning Research, Vol. 172, Zurich, Switzerland, 2022, pp. 535\u2013551, https:\/\/proceedings.mlr.press\/v172\/hosseinzadeh-taher22a.html."},{"key":"e_1_3_3_31_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.02007"},{"key":"e_1_3_3_32_2","doi-asserted-by":"publisher","unstructured":"Taslimi S. Taslimi S. Fathi N. Salehi M. Rohban M.H. Swinchex: Multi-label classification on chest X-ray images with transformers 2022 CoRR arXiv:2206.04246. doi:10.48550\/arXiv.","DOI":"10.48550\/arXiv"},{"key":"e_1_3_3_33_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.344"},{"key":"e_1_3_3_34_2","unstructured":"Wang H. Xia Y. Chestnet: A deep neural network for classification of thoracic diseases on chest radiography 2018 CoRR arXiv:1807.03058."},{"key":"e_1_3_3_35_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.369"},{"key":"e_1_3_3_36_2","doi-asserted-by":"publisher","DOI":"10.1109\/WACV56688.2023.00358"},{"key":"e_1_3_3_37_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00943"},{"key":"e_1_3_3_38_2","series-title":"Proceedings of Machine Learning Research","first-page":"2","volume-title":"Proceedings of the Machine Learning for Healthcare Conference, MLHC 2022","author":"Zhang Y.","year":"2022","unstructured":"Zhang Y., Jiang H., Miura Y., Manning C.D., Langlotz C.P., Contrastive learning of medical visual representations from paired images and text, in: Proceedings of the Machine Learning for Healthcare Conference, MLHC 2022, 5\u20136 August 2022, Lipton Z.C., Ranganath R., Sendak M.P., Sjoding M.W., Yeung S., eds, Proceedings of Machine Learning Research, Vol. 182, Durham, NC, USA, 2022, pp. 2\u201325, https:\/\/proceedings.mlr.press\/v182\/zhang22a.html."},{"key":"e_1_3_3_39_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-87234-2_33"}],"container-title":["Web Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/WEB-240279","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.3233\/WEB-240279","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/WEB-240279","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T05:27:54Z","timestamp":1777613274000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/WEB-240279"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,19]]},"references-count":38,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,8]]}},"alternative-id":["10.3233\/WEB-240279"],"URL":"https:\/\/doi.org\/10.3233\/web-240279","relation":{},"ISSN":["2405-6456","2405-6464"],"issn-type":[{"value":"2405-6456","type":"print"},{"value":"2405-6464","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,19]]}}}