{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T09:56:55Z","timestamp":1769853415674,"version":"3.49.0"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031460043","type":"print"},{"value":"9783031460050","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-46005-0_16","type":"book-chapter","created":{"date-parts":[[2023,10,7]],"date-time":"2023-10-07T04:01:36Z","timestamp":1696651296000},"page":"184-193","source":"Crossref","is-referenced-by-count":4,"title":["Multi-input Vision Transformer with\u00a0Similarity Matching"],"prefix":"10.1007","author":[{"given":"Seungeun","family":"Lee","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sung Ho","family":"Hwang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Saelin","family":"Oh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Beom Jin","family":"Park","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongwon","family":"Cho","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","reference":[{"key":"16_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"246","DOI":"10.1007\/978-3-319-46726-9_29","volume-title":"Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016","author":"O Oktay","year":"2016","unstructured":"Oktay, O., et al.: Multi-input cardiac image super-resolution using convolutional neural networks. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9902, pp. 246\u2013254. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46726-9_29"},{"key":"16_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102125","volume":"72","author":"E \u00c7all\u0131","year":"2021","unstructured":"\u00c7all\u0131, E., et al.: Deep learning for chest X-ray analysis: A survey. Med. Image Anal. 72, 102125 (2021)","journal-title":"Med. Image Anal."},{"key":"16_CR3","doi-asserted-by":"crossref","unstructured":"Mondal, S., et al.: Deep learning approach for automatic classification of x-ray images using convolutional neural network. In: 2019 Fifth International Conference on Image Information Processing (ICIIP), IEEE (2019)","DOI":"10.1109\/ICIIP47207.2019.8985687"},{"key":"16_CR4","doi-asserted-by":"publisher","unstructured":"Yu, Ke, et al.: Anatomy-guided weakly-supervised abnormality localization in chest x-rays. In: MICCAI 2022: 25th International Conference on Medical Image Computing and Computer Assisted Intervention, Part V. Springer Nature Switzerland , Cham(2022). https:\/\/doi.org\/10.1007\/978-3-031-16443-9_63","DOI":"10.1007\/978-3-031-16443-9_63"},{"key":"16_CR5","doi-asserted-by":"publisher","unstructured":"Mishra, S., et al.: Data-Driven Deep Supervision for Skin Lesion Classification. In: MICCAI 2022: 25th International Conference on Medical Image Computing and Computer Assisted Intervention, Part I. Springer Nature Switzerland, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16431-6_68","DOI":"10.1007\/978-3-031-16431-6_68"},{"key":"16_CR6","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., et al.: An image is worth 16 x 16 words: Transformers for image recognition at scale. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. IEEE, Venice (2020)"},{"issue":"11","key":"16_CR7","doi-asserted-by":"publisher","first-page":"3013","DOI":"10.3390\/jcm11113013","volume":"11","author":"M Chetoui","year":"2022","unstructured":"Chetoui, M., Akhloufi, M.A.: Explainable vision transformers and radiomics for Covid-19 detection in chest x-rays. J. Clin. Med. 11(11), 3013 (2022)","journal-title":"J. Clin. Med."},{"issue":"10","key":"16_CR8","doi-asserted-by":"publisher","first-page":"1636","DOI":"10.3390\/rs10101636","volume":"10","author":"D Duarte","year":"2018","unstructured":"Duarte, D., et al.: Multi-resolution feature fusion for image classification of building damages with convolutional neural networks. Remote Sensing 10(10), 1636 (2018)","journal-title":"Remote Sensing"},{"key":"16_CR9","doi-asserted-by":"crossref","unstructured":"Zamir, S.W., et al.: Multi-stage progressive image restoration. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2021)","DOI":"10.1109\/CVPR46437.2021.01458"},{"issue":"4","key":"16_CR10","doi-asserted-by":"publisher","first-page":"757","DOI":"10.1109\/TMI.2020.3021387","volume":"41","author":"RJ Chen","year":"2020","unstructured":"Chen, R.J., et al.: Pathomic fusion: an integrated framework for fusing histopathology and genomic features for cancer diagnosis and prognosis. IEEE Trans. Med. Imaging 41(4), 757\u2013770 (2020)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"16_CR11","doi-asserted-by":"crossref","unstructured":"Yang, S., et al.: Knowledge matters: Radiology report generation with general and specific knowledge. arXiv preprint arXiv:2112.15009 (2021)","DOI":"10.1016\/j.media.2022.102510"},{"key":"16_CR12","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., et al.: Grad-cam: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision (2017)","DOI":"10.1109\/ICCV.2017.74"},{"key":"16_CR13","unstructured":"Radford, A., et al.: Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning, PMLR (2021)"},{"key":"16_CR14","doi-asserted-by":"crossref","unstructured":"Lee, J.Y., et al.: Pediatric orbital fractures. Facial Trauma Surgery, 296\u2013303 (2020)","DOI":"10.1016\/B978-0-323-49755-8.00032-3"},{"issue":"1","key":"16_CR15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-018-37186-2","volume":"9","author":"B Park","year":"2019","unstructured":"Park, B., et al.: A curriculum learning strategy to enhance the accuracy of classification of various lesions in chest-PA X-ray screening for pulmonary abnormalities. Sci. Rep. 9(1), 1\u20139 (2019)","journal-title":"Sci. Rep."},{"key":"16_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.104750","volume":"136","author":"Y Cho","year":"2021","unstructured":"Cho, Y., et al.: Optimal number of strong labels for curriculum learning with convolutional neural network to classify pulmonary abnormalities in chest radiographs. Comput. Biol. Med. 136, 104750 (2021)","journal-title":"Comput. Biol. Med."},{"key":"16_CR17","unstructured":"Paszke, A., et al.: PyTorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32 (2019)"},{"key":"16_CR18","unstructured":"PyTorch image models Homepage. https:\/\/github.com\/rwightman\/pytorch-image-models. (Accessed 15 Feb 2023)"},{"key":"16_CR19","doi-asserted-by":"crossref","unstructured":"He, K., et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"16_CR20","first-page":"1","volume":"11","author":"J Wang","year":"2017","unstructured":"Wang, J., Perez, L.: The effectiveness of data augmentation in image classification using deep learning. Convolut. Neural Netw. Vis. Recognit. 11, 1\u20138 (2017)","journal-title":"Convolut. Neural Netw. Vis. Recognit."},{"key":"16_CR21","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: 3rd International Conference for Learning Representations (2015)"},{"key":"16_CR22","doi-asserted-by":"crossref","unstructured":"Xie, Y., et al.: When do GNNs work: understanding and improving neighborhood aggregation. In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence (2020)","DOI":"10.24963\/ijcai.2020\/181"},{"key":"16_CR23","unstructured":"Li, Y., et al.: Localvit: bringing locality to vision transformers. arXiv preprint arXiv:2104.05707 (2021)"}],"container-title":["Lecture Notes in Computer Science","Predictive Intelligence in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-46005-0_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,7]],"date-time":"2023-10-07T04:04:07Z","timestamp":1696651447000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-46005-0_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031460043","9783031460050"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-46005-0_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]}}}