{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T11:09:25Z","timestamp":1775646565416,"version":"3.50.1"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"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":["Mach. Intell. Res."],"published-print":{"date-parts":[[2026,4]]},"DOI":"10.1007\/s11633-025-1619-4","type":"journal-article","created":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T10:35:48Z","timestamp":1775644548000},"page":"444-466","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Clinically Aligned Local-global Mutual Learning Framework for Interpretable Pneumoconiosis Diagnosis"],"prefix":"10.1007","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-0035-9361","authenticated-orcid":false,"given":"Jiarui","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-5018-2850","authenticated-orcid":false,"given":"Xuerong","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5922-877X","authenticated-orcid":false,"given":"Le","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9266-4685","authenticated-orcid":false,"given":"Binglu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,4,8]]},"reference":[{"issue":"4","key":"1619_CR1","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1539\/joh.13-0149-OA","volume":"56","author":"J Xing","year":"2014","unstructured":"J. Xing, X. Huang, L. Yang, Y. Liu, H. Zhang, W. Chen. Comparison of high-resolution computerized tomography with film-screen radiography for the evaluation of opacity and the recognition of coal workers\u2019 pneumoconiosis. Journal of Occupational Health, vol. 56, no. 4, pp. 301\u2013308, 2014. DOI: https:\/\/doi.org\/10.1539\/joh.13-0149-oa.","journal-title":"Journal of Occupational Health"},{"key":"1619_CR2","doi-asserted-by":"publisher","unstructured":"L. Devnath, Z. Fan, S. Luo, P. Summons, D. Wang. Detection and visualisation of pneumoconiosis using an ensemble of multi-dimensional deep features learned from chest X-rays. International Journal of Environmental Research and Public Health, vol. 19, no. 18, Article number 11193, 2022. DOI: https:\/\/doi.org\/10.3390\/ijerph191811193.","DOI":"10.3390\/ijerph191811193"},{"key":"1619_CR3","unstructured":"Diagnosis of Occupational Pneumoconiosis, GBZ 70-2015, 2015."},{"key":"1619_CR4","volume-title":"Development of Automated Diagnostic Tools for Pneumoconiosis Detection from Chest X-ray Radiographs \u2013 The Final Report Prepared for Coal Services Health and Safety Trust","author":"Y Arzhaeva","year":"2019","unstructured":"Y. Arzhaeva, D. Wang, L. Devnath, S. Amirgholipour, R. McBean, J. Hillhouse, S. Luo, D. Meredith, K. Newbigin, D. Yates. Development of Automated Diagnostic Tools for Pneumoconiosis Detection from Chest X-ray Radiographs \u2013 The Final Report Prepared for Coal Services Health and Safety Trust. Sydney, Australia: Coal Services Health and Safety Trust, 2019."},{"key":"1619_CR5","doi-asserted-by":"publisher","unstructured":"Y. Zhang, B. Zheng, F. Zeng, X. Cheng, T. Wu, Y. Peng, Y. Zhang, Y. Xie, W. Yi, W. Chen, J. Wu, L. Li. Potential of digital chest radiography-based deep learning in screening and diagnosing pneumoconiosis: An observational study. Medicine, vol. 103, no. 25, Article number e38478, 2024. DOI: https:\/\/doi.org\/10.1097\/MD.0000000000038478.","DOI":"10.1097\/MD.0000000000038478"},{"key":"1619_CR6","volume-title":"Guidelines for the Use of the ILO International Classification of Radiographs of Pneumoconioses","author":"International Labour Organization (ILO)","year":"2022","unstructured":"International Labour Organization (ILO). Guidelines for the Use of the ILO International Classification of Radiographs of Pneumoconioses, rd ed 2022. Geneva, Switzerland: International Labour Office, International Labour Organization, 2022."},{"key":"1619_CR7","doi-asserted-by":"publisher","unstructured":"J. Wang, M. Song, D. P. Fan, X. Wang, S. Zhang, J. Yang, J. Liu, C. Wang, B. Wang. Radiologist-inspired symmetric local-global multi-supervised learning for early diagnosis of pneumoconiosis. Expert Systems with Applications, vol. 276, Article number 127173, 2025. DOI: https:\/\/doi.org\/10.1016\/j.eswa.2025.127173.","DOI":"10.1016\/j.eswa.2025.127173"},{"key":"1619_CR8","doi-asserted-by":"publisher","unstructured":"A. A. Nahid, N. Sikder, A. K. Bairagi, A. Razzaque, M. Masud, A. Z. Kouzani, M. A. P. Mahmud. A novel method to identify pneumonia through analyzing chest radiographs employing a multichannel convolutional neural network. Sensors, vol. 20, no. 12, Article number 3482, 2020. DOI: https:\/\/doi.org\/10.3390\/s20123482.","DOI":"10.3390\/s20123482"},{"key":"1619_CR9","doi-asserted-by":"publisher","unstructured":"M. Masud, A. K. Bairagi, A. A. Nahid, N. Sikder, S. Rubaiee, A. Ahmed, D. Anand. A pneumonia diagnosis scheme based on hybrid features extracted from chest radiographs using an ensemble learning algorithm. Journal of Healthcare Engineering, vol. 2021, no. 1, Article number 8862089, 2021. DOI: https:\/\/doi.org\/10.1155\/2021\/8862089.","DOI":"10.1155\/2021\/8862089"},{"key":"1619_CR10","doi-asserted-by":"crossref","unstructured":"B. Dong, W. Wang, D. P. Fan, J. Li, H. Fu, L. Shao. Polyp-PVT: Polyp segmentation with pyramid vision transformers, [Online], Available: https:\/\/arxiv.org\/abs\/2108.06932, 2024.","DOI":"10.26599\/AIR.2023.9150015"},{"key":"1619_CR11","doi-asserted-by":"publisher","unstructured":"P. Rajpurkar, J. Irvin, R. L. Ball, K. Zhu, B. Yang, H. Mehta, T. Duan, D. Ding, A. Bagul, C. P. Langlotz, B. N. Patel, K. W. Yeom, K. Shpanskaya, F. G. Blankenberg, J. Seekins, T. J. Amrhein, D. A. Mong, S. S. Halabi, E. J. Zucker, A. Y. Ng, M. P. Lungren. Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists. PLoS Medicine, vol. 15, no. 11, Article number e1002686, 2018. DOI: https:\/\/doi.org\/10.1371\/journal.pmed.1002686.","DOI":"10.1371\/journal.pmed.1002686"},{"key":"1619_CR12","doi-asserted-by":"publisher","unstructured":"L. Devnath, S. Luo, P. Summons, D. Wang, K. Shaukat, I. A. Hameed, F. S. Alrayes. Deep ensemble learning for the automatic detection of pneumoconiosis in coal worker\u2019s chest X-ray radiography. Journal of Clinical Medicine, vol. 11, no. 18, Article number 5342, 2022. DOI: https:\/\/doi.org\/10.3390\/jcm11185342.","DOI":"10.3390\/jcm11185342"},{"key":"1619_CR13","doi-asserted-by":"publisher","unstructured":"X. Ren, S. Chu, G. Ji, Z. Zhao, J. Zhao, Y. Qiang, Y. Wei, Y. Wang. OMSF2: Optimizing multi-scale feature fusion learning for pneumoconiosis staging diagnosis through data specificity augmentation. Complex & Intelligent Systems, vol. 11, no. 1, Article number 109, 2025. DOI: https:\/\/doi.org\/10.1007\/s40747-024-01729-0.","DOI":"10.1007\/s40747-024-01729-0"},{"key":"1619_CR14","doi-asserted-by":"publisher","unstructured":"Q. Zhao, Y. Guo, D. Gong. RAFFCDNet: A diagnostic network based on adaptive feature fusion and class-distance-based loss function for occupational pneumoconiosis. Biomedical Signal Processing and Control, vol. 109, Article number 107957, 2025. DOI: https:\/\/doi.org\/10.1016\/j.bspc.2025.107957.","DOI":"10.1016\/j.bspc.2025.107957"},{"key":"1619_CR15","doi-asserted-by":"publisher","DOI":"10.1109\/IVCNZ48456.2019.8960961","volume-title":"Proceedings of International Conference on Image and Vision Computing New Zealand","author":"L Devnath","year":"2019","unstructured":"L. Devnath, S. Luo, P. Summons, D. Wang. An accurate black lung detection using transfer learning based on deep neural networks. In Proceedings of International Conference on Image and Vision Computing New Zealand, IEEE, Dunedin, New Zealand, 2019. DOI: https:\/\/doi.org\/10.1109\/IVCNZ48456.2019.8960961."},{"issue":"4","key":"1619_CR16","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1007\/s11633-022-1349-9","volume":"19","author":"D P Fan","year":"2022","unstructured":"D. P. Fan, Z. Huang, P. Zheng, H. Liu, X. Qin, L. Van Gool. Facial-sketch synthesis: A new challenge. Machine Intelligence Research, vol. 19, no. 4, pp. 257\u2013287, 2022. DOI: https:\/\/doi.org\/10.1007\/s11633-022-1349-9.","journal-title":"Machine Intelligence Research"},{"key":"1619_CR17","doi-asserted-by":"publisher","first-page":"2242","DOI":"10.1109\/ICCV.2017.244","volume-title":"Proceedings of IEEE International Conference on Computer Vision","author":"J Y Zhu","year":"2017","unstructured":"J. Y. Zhu, T. Park, P. Isola, A. A. Efros. Unpaired image-to-image translation using cycle-consistent adversarial networks. In Proceedings of IEEE International Conference on Computer Vision, Venice, Italy, pp. 2242\u20132251, 2017. DOI: https:\/\/doi.org\/10.1109\/ICCV.2017.244."},{"issue":"8","key":"1619_CR18","doi-asserted-by":"publisher","first-page":"4353","DOI":"10.1364\/BOE.461888","volume":"13","author":"L Fan","year":"2022","unstructured":"L. Fan, Z. Wang, J. Zhou. LDADN: A local discriminant auxiliary disentangled network for key-region-guided chest X-ray image synthesis augmented in pneumoconiosis detection. Biomedical Optics Express, vol. 13, no. 8, pp. 4353\u20134369, 2022. DOI: https:\/\/doi.org\/10.1364\/BOE.461888.","journal-title":"Biomedical Optics Express"},{"key":"1619_CR19","doi-asserted-by":"publisher","unstructured":"W. Sun, D. Wu, Y. Luo, L. Liu, H. Zhang, S. Wu, Y. Zhang, C. Wang, H. Zheng, J. Shen, C. Luo. ExpertNet: Defeat noisy labels by deep expert consultation paradigm for pneumoconiosis staging on chest radiographs. Expert Systems with Applications, vol. 232, Article number 120710, 2023. DOI: https:\/\/doi.org\/10.1016\/j.eswa.2023.120710.","DOI":"10.1016\/j.eswa.2023.120710"},{"key":"1619_CR20","doi-asserted-by":"publisher","unstructured":"M. Song, J. Wang, Z. Yu, J. Wang, L. Yang, Y. Lu, B. Li, X. Wang, X. Wang, Q. Huang, Z. Li, N. I. Kanellakis, J. Liu, J. Wang, B. Wang, J. Yang. PneumoLLM: Harnessing the power of large language model for pneumoconiosis diagnosis. Medical Image Analysis, vol. 97, Article number 103248, 2024. DOI: https:\/\/doi.org\/10.1016\/j.media.2024.103248.","DOI":"10.1016\/j.media.2024.103248"},{"key":"1619_CR21","doi-asserted-by":"publisher","first-page":"618","DOI":"10.1109\/ICCV.2017.74","volume-title":"Proceedings of the IEEE International Conference on Computer Vision","author":"R R Selvaraju","year":"2017","unstructured":"R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, D. Batra. Grad-CAM: Visual explanations from deep networks via gradient-based localization. In Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy, pp. 618\u2013626, 2017. DOI: https:\/\/doi.org\/10.1109\/ICCV.2017.74."},{"issue":"4","key":"1619_CR22","doi-asserted-by":"publisher","first-page":"1095","DOI":"10.1109\/TMI.2022.3224067","volume":"42","author":"C Ouyang","year":"2023","unstructured":"C. Ouyang, C. Chen, S. Li, Z. Li, C. Qin, W. Bai, D. Rueckert. Causality-inspired single-source domain generalization for medical image segmentation. IEEE Transactions on Medical Imaging, vol. 42, no. 4, pp. 1095\u20131106, 2023. DOI: https:\/\/doi.org\/10.1109\/TMI.2022.3224067.","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"1619_CR23","doi-asserted-by":"publisher","first-page":"6561","DOI":"10.1109\/BIBM62325.2024.10822683","volume-title":"Proceedings of IEEE International Conference on Bioinformatics and Biomedicine","author":"T Ruga","year":"2024","unstructured":"T. Ruga, E. Vocaturo, E. Zumpano. Explainable deep learning for chest X-ray classification. In Proceedings of IEEE International Conference on Bioinformatics and Biomedicine, Lisbon, Portugal, pp. 6561\u20136566, 2024. DOI: https:\/\/doi.org\/10.1109\/BIBM62325.2024.10822683."},{"key":"1619_CR24","doi-asserted-by":"publisher","unstructured":"A. N. Mir, D. R. Rizvi. Advancements in deep learning and explainable artificial intelligence for enhanced medical image analysis: A comprehensive survey and future directions. Engineering Applications of Artificial Intelligence, vol. 158, Article number 111413, 2025. DOI: https:\/\/doi.org\/10.1016\/j.engappai.2025.111413.","DOI":"10.1016\/j.engappai.2025.111413"},{"key":"1619_CR25","doi-asserted-by":"publisher","first-page":"458","DOI":"10.1007\/978-3-031-88223-4_32","volume-title":"Proceedings of the International Conference on Pattern Recognition ICPR International Workshops and Challenges","author":"\u00c6 P P Vidal","year":"2025","unstructured":"\u00c6. P. Vidal, A. S. Johansen, M. N. S. Jahromi, S. Escalera, K. Nasrollahi, T. B. Moeslund. Verifying machine unlearning with explainable AI. In Proceedings of the International Conference on Pattern Recognition ICPR International Workshops and Challenges, Kolkata, India, pp. 458\u2013473, 2025. DOI: https:\/\/doi.org\/10.1007\/978-3-031-88223-4_32."},{"key":"1619_CR26","doi-asserted-by":"publisher","first-page":"7278","DOI":"10.1109\/TMM.2024.3363660","volume":"26","author":"X Liu","year":"2024","unstructured":"X. Liu, Y. Zhang, Z. Yu, H. Lu, H. Yue, J. Yang. rPPGMAE: Self-supervised pretraining with masked autoencoders for remote physiological measurements. IEEE Transactions on Multimedia, vol. 26, pp. 7278\u20137293, 2024. DOI: https:\/\/doi.org\/10.1109\/TMM.2024.3363660.","journal-title":"IEEE Transactions on Multimedia"},{"issue":"10","key":"1619_CR27","doi-asserted-by":"publisher","first-page":"6311","DOI":"10.1109\/TPAMI.2021.3091167","volume":"44","author":"Y Qin","year":"2022","unstructured":"Y. Qin, Z. Yu, L. Yan, Z. Wang, C. Zhao, Z. Lei. Meta-teacher for face anti-spoofing. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 10, pp. 6311\u20136326, 2022. DOI: https:\/\/doi.org\/10.1109\/TPAMI.2021.3091167.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"1619_CR28","doi-asserted-by":"publisher","first-page":"9650","DOI":"10.1109\/ICCV48922.2021.00951","volume-title":"Proceedings of IEEE\/CVF International Conference on Computer Vision","author":"M Caron","year":"2021","unstructured":"M. Caron, H. Touvron, I. Misra, H. J\u00e9gou, J. Mairal, P. Bojanowski, A. Joulin. Emerging properties in self-supervised vision transformers. In Proceedings of IEEE\/CVF International Conference on Computer Vision, IEEE, Montreal, Canada, pp. 9650\u20139660, 2021. DOI: https:\/\/doi.org\/10.1109\/ICCV48922.2021.00951."},{"key":"1619_CR29","unstructured":"M. Oquab, T. Darcet, T. Moutakanni, H. V. Vo, M. Szafraniec, V. Khalidov, P. Fernandez, D. Haziza, F. Massa, A. El-Nouby, M. Assran, N. Ballas, W. Galuba, R. Howes, P. Y. Huang, S. W. Li, I. Misra, M. Rabbat, V. Sharma, G. Synnaeve, H. Xu, H. J\u00e9gou, J. Mairal, P. Labatut, A. Joulin, P. Bojanowski. DINOv2: Learning robust visual features without supervision. Transactions on Machine Learning Research, to be published."},{"key":"1619_CR30","doi-asserted-by":"publisher","first-page":"16865","DOI":"10.1109\/CVPR52688.2022.01638","volume-title":"Proceedings of IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"P T Jiang","year":"2022","unstructured":"P. T. Jiang, Y. Yang, Q. Hou, Y. Wei. L2G: A simple local-to-global knowledge transfer framework for weakly supervised semantic segmentation. In Proceedings of IEEE\/CVF Conference on Computer Vision and Pattern Recognition, IEEE, New Orleans, USA, pp. 16865\u201316875, 2022. DOI: https:\/\/doi.org\/10.1109\/CVPR52688.2022.01638."},{"key":"1619_CR31","doi-asserted-by":"publisher","unstructured":"H. Cheng, X. Liu, J. Zhang, X. Dong, X. Ma, Y. Zhang, H. Meng, X. Chen, G. Yue, Y. Li, Y. Wu. GLMKD: Joint global and local mutual knowledge distillation for weakly supervised lesion segmentation in histopathology images. Expert Systems with Applications, vol. 279, Article number 127425, 2025. DOI: https:\/\/doi.org\/10.1016\/j.eswa.2025.127425.","DOI":"10.1016\/j.eswa.2025.127425"},{"key":"1619_CR32","doi-asserted-by":"publisher","first-page":"481","DOI":"10.1007\/978-3-030-87196-3_45","volume-title":"Proceedings of the 24th International Conference on Medical Image Computing and Computer Assisted Intervention","author":"X Hu","year":"2021","unstructured":"X. Hu, D. Zeng, X. Xu, Y. Shi. Semi-supervised contrastive learning for label-efficient medical image segmentation. In Proceedings of the 24th International Conference on Medical Image Computing and Computer Assisted Intervention, Strasbourg, France, pp. 481\u2013490, 2021. DOI: https:\/\/doi.org\/10.1007\/978-3-030-87196-3_45."},{"key":"1619_CR33","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1007\/978-3-030-87583-1_15","volume-title":"Proceedings of the Second International Workshop on Simplifying Medical Ultrasound","author":"A Chartsias","year":"2021","unstructured":"A. Chartsias, S. Gao, A. Mumith, J. Oliveira, K. Bhatia, B. Kainz, A. Beqiri. Contrastive learning for view classification of echocardiograms. In Proceedings of the Second International Workshop on Simplifying Medical Ultrasound, Strasbourg, France, pp. 149\u2013158, 2021. DOI: https:\/\/doi.org\/10.1007\/978-3-030-87583-1_15."},{"issue":"11","key":"1619_CR34","doi-asserted-by":"publisher","first-page":"5518","DOI":"10.1109\/JBHI.2022.3199594","volume":"26","author":"U Kamal","year":"2022","unstructured":"U. Kamal, M. Zunaed, N. B. Nizam, T. Hasan. Anatomy-XNet: An anatomy aware convolutional neural network for thoracic disease classification in chest X-rays. IEEE Journal of Biomedical and Health Informatics, vol. 26, no. 11, pp. 5518\u20135528, 2022. DOI: https:\/\/doi.org\/10.1109\/JBHI.2022.3199594.","journal-title":"IEEE Journal of Biomedical and Health Informatics"},{"issue":"7","key":"1619_CR35","doi-asserted-by":"publisher","first-page":"2016","DOI":"10.1109\/JBHI.2019.2952597","volume":"24","author":"B Chen","year":"2020","unstructured":"B. Chen, J. Li, G. Lu, D. Zhang. Lesion location attention guided network for multi-label thoracic disease classification in chest X-rays. IEEE Journal of Biomedical and Health Informatics, vol. 24, no. 7, pp. 2016\u20132027, 2020. DOI: https:\/\/doi.org\/10.1109\/JBHI.2019.2952597.","journal-title":"IEEE Journal of Biomedical and Health Informatics"},{"key":"1619_CR36","doi-asserted-by":"publisher","first-page":"1306","DOI":"10.1109\/ICCV48922.2021.00136","volume-title":"Proceedings of IEEE\/CVF International Conference on Computer Vision","author":"C Wang","year":"2021","unstructured":"C. Wang, J. Xiao, Y. Han, Q. Yang, S. Song, G. Huang. Towards learning spatially discriminative feature representations. In Proceedings of IEEE\/CVF International Conference on Computer Vision, IEEE, Montreal, Canada, pp. 1306\u20131315, 2021. DOI: https:\/\/doi.org\/10.1109\/ICCV48922.2021.00136."},{"key":"1619_CR37","doi-asserted-by":"publisher","unstructured":"H. Zhang, L. Chen, X. Gu, M. Zhang, Y. Qin, F. Yao, Z. Wang, Y. Gu, G. Z. Yang. Trustworthy learning with (un)sure annotation for lung nodule diagnosis with CT. Medical Image Analysis, vol. 83, Article number 102627, 2023. DOI: https:\/\/doi.org\/10.1016\/j.media.2022.102627.","DOI":"10.1016\/j.media.2022.102627"},{"key":"1619_CR38","doi-asserted-by":"publisher","first-page":"2921","DOI":"10.1109\/CVPR.2016.319","volume-title":"Proceedings of IEEE Conference on Computer Vision and Pattern Recognition","author":"B Zhou","year":"2016","unstructured":"B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, A. Torralba. Learning deep features for discriminative localization. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, pp. 2921\u20132929, 2016. DOI: https:\/\/doi.org\/10.1109\/CVPR.2016.319."},{"key":"1619_CR39","doi-asserted-by":"publisher","first-page":"15538","DOI":"10.1109\/CVPR52734.2025.01448","volume-title":"Proceedings of IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"D Pitawela","year":"2025","unstructured":"D. Pitawela, G. Carneiro, H. T. Chen. CLOC: Contrastive learning for ordinal classification with multi-margin N-pair loss. In Proceedings of IEEE\/CVF Conference on Computer Vision and Pattern Recognition, IEEE, Nashville, USA, pp. 15538\u201315548, 2025. DOI: https:\/\/doi.org\/10.1109\/CVPR52734.2025.01448."},{"issue":"1","key":"1619_CR40","doi-asserted-by":"publisher","first-page":"71","DOI":"10.2214\/ajr.174.1.1740071","volume":"174","author":"J Shiraishi","year":"2000","unstructured":"J. Shiraishi, S. Katsuragawa, J. Ikezoe, T. Matsumoto, T. Kobayashi, K. Komatsu, M. Matsui, H. Fujita, Y. Kodera, K. Doi. Development of a digital image database for chest radiographs with and without a lung nodule: Receiver operating characteristic analysis of radiologists\u2019 detection of pulmonary nodules. American Journal of Roentgenology, vol. 174, no. 1, pp. 71\u201374, 2000. DOI: https:\/\/doi.org\/10.2214\/ajr.174.1.1740071.","journal-title":"American Journal of Roentgenology"},{"key":"1619_CR41","doi-asserted-by":"publisher","unstructured":"Q. Zhang, S. Zhang, Y. Pan, L. Sun, J. Li, Y. Qiao, J. Zhao, X. Wang, Y. Feng, Y. Zhao, Z. Zheng, X. Yang, L. Liu, C. Qin, K. Zhao, X. Liu, C. Li, L. Zhang, C. Yang, N. Zhuo, H. Zhang, J. Liu, J. Gao, X. Di, F. Meng, L. Zhang, Y. Wang, Y. Duan, H. Shen, Y. Li, M. Yang, Y. Yang, X. Xin, X. Wei, X. Zhou, R. Jin, L. Zhang, X. Wang, F. Song, X. Zheng, M. Gao, K. Chen, X. Li. Deep learning to diagnose Hashimoto\u2019s thyroiditis from sonographic images. Nature Communications, vol. 13, no. 1, Article number 3759, 2022. DOI: https:\/\/doi.org\/10.1038\/s41467-022-31449-3.","DOI":"10.1038\/s41467-022-31449-3"},{"issue":"1","key":"1619_CR42","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1177\/001316446002000104","volume":"20","author":"J Cohen","year":"1960","unstructured":"J. Cohen. A coefficient of agreement for nominal scales. Educational and Psychological Measurement, vol. 20, no. 1, pp. 37\u201346, 1960. DOI: https:\/\/doi.org\/10.1177\/001316446002000104.","journal-title":"Educational and Psychological Measurement"},{"key":"1619_CR43","doi-asserted-by":"publisher","unstructured":"N. Gaggion, C. Mosquera, L. Mansilla, J. M. Saidman, M. Aineseder, D. H. Milone, E. Ferrante. CheXmask: A large-scale dataset of anatomical segmentation masks for multi-center chest X-ray images. Scientific Data, vol. 11, no. 1, Article number 511, 2024. DOI: https:\/\/doi.org\/10.1038\/s41597-024-03358-1.","DOI":"10.1038\/s41597-024-03358-1"},{"key":"1619_CR44","doi-asserted-by":"publisher","first-page":"11966","DOI":"10.1109\/CVPR52688.2022.01167","volume-title":"Proceedings of IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Z Liu","year":"2022","unstructured":"Z. Liu, H. Mao, C. Y. Wu, C. Feichtenhofer, T. Darrell, S. Xie. A ConvNet for the 2020s. In Proceedings of IEEE\/CVF Conference on Computer Vision and Pattern Recognition, IEEE, New Orleans, USA, pp. 11966\u201311976, 2022. DOI: https:\/\/doi.org\/10.1109\/CVPR52688.2022.01167."},{"key":"1619_CR45","doi-asserted-by":"publisher","first-page":"770","DOI":"10.1109\/CVPR.2016.90","volume-title":"Proceedings of IEEE Conference on Computer Vision and Pattern Recognition","author":"K He","year":"2016","unstructured":"K. He, X. Zhang, S. Ren, J. Sun. Deep residual learning for image recognition. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, pp. 770\u2013778, 2016. DOI: https:\/\/doi.org\/10.1109\/CVPR.2016.90."},{"key":"1619_CR46","volume-title":"Proceedings of the 9th International Conference on Learning Representations","author":"A Dosovitskiy","year":"2021","unstructured":"A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby. An image is worth 16\u00d716 words: Transformers for image recognition at scale. In Proceedings of the 9th International Conference on Learning Representations, 2021."},{"key":"1619_CR47","doi-asserted-by":"publisher","first-page":"10012","DOI":"10.1109\/ICCV48922.2021.00986","volume-title":"Proceedings of IEEE\/CVF International Conference on Computer Vision","author":"Z Liu","year":"2021","unstructured":"Z. Liu, Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, S. Lin, B. Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of IEEE\/CVF International Conference on Computer Vision, IEEE, Montreal, Canada, pp. 10012\u201310022, 2021. DOI: https:\/\/doi.org\/10.1109\/ICCV48922.2021.00986."},{"key":"1619_CR48","volume-title":"Proceedings of the 38th International Conference on Neural Information Processing Systems","author":"Y Liu","year":"2024","unstructured":"Y. Liu, Y. Tian, Y. Zhao, H. Yu, L. Xie, Y. Wang, Q. Ye, J. Jiao, Y. Liu. VMamba: Visual state space model. In Proceedings of the 38th International Conference on Neural Information Processing Systems, Vancouver, Canada, Article number 3273, 2024."},{"key":"1619_CR49","doi-asserted-by":"publisher","first-page":"4484","DOI":"10.1109\/CVPR52734.2025.00423","volume-title":"Proceedings of IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"W Yu","year":"2025","unstructured":"W. Yu, X. Wang. MambaOut: Do we really need mamba for vision? In Proceedings of IEEE\/CVF Conference on Computer Vision and Pattern Recognition, IEEE, Nashville, USA, pp. 4484\u20134496, 2025. DOI: https:\/\/doi.org\/10.1109\/CVPR52734.2025.00423."},{"key":"1619_CR50","unstructured":"W. Ye, J. Yao, H. Xue, Y. Li. Weakly supervised lesion localization with probabilistic-CAM pooling, [Online], Available: https:\/\/arxiv.org\/abs\/2005.14480, 2020."},{"issue":"1","key":"1619_CR51","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1109\/JBHI.2023.3324333","volume":"28","author":"Y Fan","year":"2024","unstructured":"Y. Fan, H. Gong. An improved COVID-19 classification model on chest radiography by dual-ended multiple attention learning. IEEE Journal of Biomedical and Health Informatics, vol. 28, no. 1, pp. 145\u2013156, 2024. DOI: https:\/\/doi.org\/10.1109\/JBHI.2023.3324333.","journal-title":"IEEE Journal of Biomedical and Health Informatics"},{"issue":"7","key":"1619_CR52","doi-asserted-by":"publisher","first-page":"3513","DOI":"10.1109\/JBHI.2023.3267057","volume":"27","author":"Y Fu","year":"2023","unstructured":"Y. Fu, P. Xue, Z. Zhang, E. Dong. PKA2-net: Prior knowledge-based active attention network for accurate pneumonia diagnosis on chest X-ray images. IEEE Journal of Biomedical and Health Informatics, vol. 27, no. 7, pp. 3513\u20133524, 2023. DOI: https:\/\/doi.org\/10.1109\/JBHI.2023.3267057.","journal-title":"IEEE Journal of Biomedical and Health Informatics"},{"key":"1619_CR53","unstructured":"Y. Yue, Z. Li. MedMamba: Vision mamba for medical image classification, [Online], Available: https:\/\/arxiv.org\/abs\/2403.03849, 2024."},{"key":"1619_CR54","first-page":"933","volume-title":"Proceedings of the 34th International Conference on Machine Learning","author":"Y N Dauphin","year":"2017","unstructured":"Y. N. Dauphin, A. Fan, M. Auli, D. Grangier. Language modeling with gated convolutional networks. In Proceedings of the 34th International Conference on Machine Learning, Sydney, Australia, pp. 933\u2013941, 2017."}],"container-title":["Machine Intelligence Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11633-025-1619-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11633-025-1619-4","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11633-025-1619-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T10:35:55Z","timestamp":1775644555000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11633-025-1619-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4]]},"references-count":54,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026,4]]}},"alternative-id":["1619"],"URL":"https:\/\/doi.org\/10.1007\/s11633-025-1619-4","relation":{},"ISSN":["2731-538X","2731-5398"],"issn-type":[{"value":"2731-538X","type":"print"},{"value":"2731-5398","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4]]},"assertion":[{"value":"21 July 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 November 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 April 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"This research has not, to the best of the researchers\u2019 knowledge, violated the guidelines\/principals of the Declaration of Helsinki and its amendments.All procedures involving human participants were conducted in accordance with applicable ethical standards and relevant institutional and national regulations.Prior to use in this study, the dataset was rigorously anonymized and de-identified. All personally identifiable information, was permanently removed to eliminate any risk of participant identification. The study complies with all relevant data protection laws and data use agreements.The private dataset was accessed and utilized strictly within the scope of authorization granted by the data owner and solely for the purposes described in this study. The data were neither disclosed to third parties nor used for commercial purposes, and no use beyond the approved research scope was permitted.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Statement"}},{"value":"The authors declared that they have no conflicts of interest to this work.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations of conflict of interest"}}]}}