{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T02:19:29Z","timestamp":1785377969138,"version":"3.55.0"},"reference-count":57,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,5,9]],"date-time":"2023-05-09T00:00:00Z","timestamp":1683590400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,5,9]],"date-time":"2023-05-09T00:00:00Z","timestamp":1683590400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["21K07656"],"award-info":[{"award-number":["21K07656"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002241","name":"Japan Science and Technology Agency","doi-asserted-by":"publisher","award":["ERATO JPMJER 2102"],"award-info":[{"award-number":["ERATO JPMJER 2102"]}],"id":[{"id":"10.13039\/501100002241","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Imaging"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>This study was conducted to alleviate a common difficulty in chest X-ray image diagnosis: The attention region in a convolutional neural network (CNN) does not often match the doctor\u2019s point of focus. The method presented herein, which guides the area of attention in CNN to a medically plausible region, can thereby improve diagnostic capabilities.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>The model is based on an attention branch network, which has excellent interpretability of the classification model. This model has an additional new operation branch that guides the attention region to the lung field and heart in chest X-ray images. We also used three chest X-ray image datasets (Teikyo, Tokushima, and ChestX-ray14) to evaluate the CNN attention area of interest in these fields. Additionally, after devising a quantitative method of evaluating improvement of a CNN\u2019s region of interest, we applied it to evaluation of the proposed model.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>Operation branch networks maintain or improve the area under the curve to a greater degree than conventional CNNs do. Furthermore, the network better emphasizes reasonable anatomical parts in chest X-ray images.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>The proposed network better emphasizes the reasonable anatomical parts in chest X-ray images. This method can enhance capabilities for image interpretation based on judgment.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12880-023-01019-0","type":"journal-article","created":{"date-parts":[[2023,5,10]],"date-time":"2023-05-10T07:31:40Z","timestamp":1683703900000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Classification of chest X-ray images by incorporation of medical domain knowledge into operation branch networks"],"prefix":"10.1186","volume":"23","author":[{"given":"Takumasa","family":"Tsuji","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yukina","family":"Hirata","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kenya","family":"Kusunose","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Masataka","family":"Sata","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shinobu","family":"Kumagai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kenshiro","family":"Shiraishi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun\u2019ichi","family":"Kotoku","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,5,9]]},"reference":[{"key":"1019_CR1","unstructured":"Rajpurkar P, Irvin J, Zhu K, et al.: CheXNet: Radiologist-level pneumonia detection on chest X-rays with deep learning. ArXiv. 2017; published online Nov 14. http:\/\/arxiv.org\/abs\/1711.05225 (preprint)"},{"key":"1019_CR2","doi-asserted-by":"publisher","first-page":"113909","DOI":"10.1016\/j.eswa.2020.113909","volume":"165","author":"TB Chandra","year":"2021","unstructured":"Chandra TB, Verma K, Singh BK, et al. Coronavirus disease (COVID-19) detection in chest X-Ray images using majority voting based classifier ensemble. Expert Syst Appl. 2021;165:113909.","journal-title":"Expert Syst Appl"},{"key":"1019_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.114054","volume":"164","author":"AM Ismael","year":"2021","unstructured":"Ismael AM, \u015eeng\u00fcr A. Deep learning approaches for COVID-19 detection based on chest X-ray images. Expert Syst Appl. 2021;164: 114054.","journal-title":"Expert Syst Appl"},{"key":"1019_CR4","doi-asserted-by":"publisher","first-page":"118029","DOI":"10.1016\/j.eswa.2022.118029","volume":"207","author":"H Li","year":"2022","unstructured":"Li H, Zeng N, Wu P, et al. Cov-Net: a computer-aided diagnosis method for recognizing COVID-19 from chest X-ray images via machine vision. Expert Syst Appl. 2022;207:118029.","journal-title":"Expert Syst Appl"},{"key":"1019_CR5","doi-asserted-by":"publisher","first-page":"19549","DOI":"10.1038\/s41598-020-76550-z","volume":"10","author":"L Wang","year":"2020","unstructured":"Wang L, Lin ZQ, Wong A. COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images. Sci Rep. 2020;10:19549.","journal-title":"Sci Rep"},{"key":"1019_CR6","doi-asserted-by":"publisher","first-page":"1077","DOI":"10.1007\/s00354-022-00172-4","volume":"40","author":"M Yildirim","year":"2022","unstructured":"Yildirim M, Ero\u011flu O, Ero\u011flu Y, et al. COVID-19 detection on chest X-ray images with the proposed model using artificial intelligence and classifiers. New Gener Comput. 2022;40:1077\u201391.","journal-title":"New Gener Comput"},{"key":"1019_CR7","doi-asserted-by":"crossref","unstructured":"Zhou B, Khosla A, Lapedriza A, et al.: Learning Deep Features for Discriminative Localization. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR). IEEE; 2016. p. 2921\u20139.","DOI":"10.1109\/CVPR.2016.319"},{"key":"1019_CR8","doi-asserted-by":"crossref","unstructured":"Selvaraju RR, Cogswell M, Das A, et al.: Grad-CAM: Visual explanations from deep networks via gradient-based localization. In: 2017 IEEE international conference on computer vision (ICCV). IEEE; 2017. p. 618\u201326.","DOI":"10.1109\/ICCV.2017.74"},{"key":"1019_CR9","unstructured":"Smilkov D, Thorat N, Kim B, et al.: SmoothGrad: removing noise by adding noise. arXiv preprint arXiv:1706.03825, 2017."},{"key":"1019_CR10","doi-asserted-by":"crossref","unstructured":"Ribeiro MT, Singh S, Guestrin C. \u201cWhy should i trust you?\u201d: explaining the predictions of any classifier. 2016; published\nAug 9. https:\/\/arxiv.org\/abs\/1602.04938 (preprint).","DOI":"10.1145\/2939672.2939778"},{"key":"1019_CR11","first-page":"4766","volume":"2","author":"S Lundberg","year":"2017","unstructured":"Lundberg S, Lee S-I. A unified approach to interpreting model predictions. Adv Neural Inf Process Syst. 2017;2:4766\u201375.","journal-title":"Adv Neural Inf Process Syst"},{"key":"1019_CR12","doi-asserted-by":"publisher","first-page":"2108","DOI":"10.1016\/j.jid.2018.06.175","volume":"138","author":"A Narla","year":"2018","unstructured":"Narla A, Kuprel B, Sarin K, et al. Automated classification of skin lesions: from pixels to practice. J Investig Dermatol. 2018;138:2108\u201310.","journal-title":"J Investig Dermatol"},{"key":"1019_CR13","doi-asserted-by":"publisher","first-page":"e1002683","DOI":"10.1371\/journal.pmed.1002683","volume":"15","author":"JR Zech","year":"2018","unstructured":"Zech JR, Badgeley MA, Liu M, et al. Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: a cross-sectional study. PLoS Med. 2018;15:e1002683.","journal-title":"PLoS Med"},{"key":"1019_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.101985","author":"X Xie","year":"2020","unstructured":"Xie X, Niu J, Liu X, et al. A survey on incorporating domain knowledge into deep learning for medical image analysis. Med. Image Anal. 2020. https:\/\/doi.org\/10.1016\/j.media.2021.101985.","journal-title":"Med Image Anal"},{"key":"1019_CR15","unstructured":"Guan Q, Huang Y, Zhong Z, et al.: Diagnose like a radiologist: attention guided convolutional neural network for thorax disease classification. ArXiv180109927 Cs [Internet]. 2018 Jan 30. Available from: http:\/\/arxiv.org\/abs\/1801.09927."},{"key":"1019_CR16","doi-asserted-by":"publisher","first-page":"348","DOI":"10.1166\/jmihi.2020.2901","volume":"10","author":"X Huang","year":"2019","unstructured":"Huang X, Fang Y, Lu M, et al. Dual-ray net: automatic diagnosis of thoracic diseases using frontal and lateral chest X-rays. J Med Imaging Health Inform. 2019;10:348\u201355.","journal-title":"J Med Imaging Health Inform"},{"key":"1019_CR17","doi-asserted-by":"publisher","first-page":"3429","DOI":"10.1109\/TMI.2020.2995518","volume":"39","author":"Q Liu","year":"2020","unstructured":"Liu Q, Yu L, Luo L, et al. Semi-supervised medical image classification with relation-driven self-ensembling model. IEEE Trans Med Imaging. 2020;39:3429\u201340.","journal-title":"IEEE Trans Med Imaging"},{"key":"1019_CR18","unstructured":"D\u00edaz IG: Incorporating the knowledge of dermatologists to convolutional neural networks for the diagnosis of skin lesions. International Skin Imaging Collaboration (ISIC) 2017 Challenge at the International Symposium on Biomedical Imaging (ISBI)."},{"key":"1019_CR19","unstructured":"Li L, Xu M, Wang X, et al.: attention based glaucoma detection: a large-scale database and CNN model."},{"key":"1019_CR20","doi-asserted-by":"crossref","unstructured":"Mitsuhara M, Fukui H, Sakashita Y, et al.: Embedding human knowledge into deep neural network via attention map. In: VISIGRAPP 2021 \u2013 Proceedings of the 16th international joint conference on computer vision, imaging and computer graphics theory and applications. 2019;5:626\u201336.","DOI":"10.5220\/0010335806260636"},{"key":"1019_CR21","doi-asserted-by":"crossref","unstructured":"Kamal U, Zunaed M, Nizam NB, et al. Anatomy X-net: a semi-supervised anatomy aware convolutional neural network for thoracic disease classification. IEEE J Biomed Health Inform 2022;1\u201311.","DOI":"10.1109\/JBHI.2022.3199594"},{"key":"1019_CR22","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-021-08050-1","author":"D Keidar","year":"2021","unstructured":"Keidar D, Yaron D, Goldstein E, et al. COVID-19 classification of X-ray images using deep neural networks. Eur Radiol 2021:31:9654-9663. https:\/\/doi.org\/10.1007\/s00330-021-08050-1.","journal-title":"Eur Radiol"},{"key":"1019_CR23","first-page":"100138","volume":"6","author":"D Arias-Garz\u00f3n","year":"2021","unstructured":"Arias-Garz\u00f3n D, Alzate-Grisales JA, Orozco-Arias S, et al. COVID-19 detection in X-ray images using convolutional neural networks. Mach Learn Appl. 2021;6:100138.","journal-title":"Mach Learn Appl"},{"key":"1019_CR24","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1016\/j.compmedimag.2019.05.005","volume":"75","author":"H Liu","year":"2019","unstructured":"Liu H, Wang L, Nan Y, et al. SDFN: Segmentation-based deep fusion network for thoracic disease classification in chest X-ray images. Comput Med Imaging Graph. 2019;75:66\u201373.","journal-title":"Comput Med Imaging Graph"},{"key":"1019_CR25","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1016\/j.neucom.2021.03.034","volume":"443","author":"Y Xu","year":"2021","unstructured":"Xu Y, Lam HK, Jia G. MANet: A two-stage deep learning method for classification of COVID-19 from Chest X-ray images. Neurocomputing. 2021;443:96\u2013105.","journal-title":"Neurocomputing"},{"key":"1019_CR26","doi-asserted-by":"crossref","unstructured":"Fukui H, Hirakawa T, Yamashita T, et al.: Attention branch network: learning of attention mechanism for visual explanation. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition. 2018; 10697\u2013706.","DOI":"10.1109\/CVPR.2019.01096"},{"key":"1019_CR27","doi-asserted-by":"crossref","unstructured":"Wang X, Peng Y, Lu L, et al.: ChestX-Ray8: hospital-scale chest X-ray database and benchmarks on weakly supervised classification and localization of common thorax diseases. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR). IEEE; 2017. p. 3462\u201371.","DOI":"10.1109\/CVPR.2017.369"},{"key":"1019_CR28","doi-asserted-by":"publisher","first-page":"1801897","DOI":"10.1183\/13993003.01897-2018","volume":"53","author":"J-L Vachi\u00e9ry","year":"2019","unstructured":"Vachi\u00e9ry J-L, Tedford RJ, Rosenkranz S, et al. Pulmonary hypertension due to left heart disease. Eur Respir J. 2019;53:1801897.","journal-title":"Eur Respir J"},{"key":"1019_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1183\/13993003.01904-2018","volume":"53","author":"A Frost","year":"2019","unstructured":"Frost A, Badesch D, Gibbs JSR, et al. Diagnosis of pulmonary hypertension. Eur Respir J. 2019;53:1\u201312.","journal-title":"Eur Respir J"},{"key":"1019_CR30","doi-asserted-by":"publisher","first-page":"19311","DOI":"10.1038\/s41598-020-76359-w","volume":"10","author":"K Kusunose","year":"2020","unstructured":"Kusunose K, Hirata Y, Tsuji T, et al. Deep learning to predict elevated pulmonary artery pressure in patients with suspected pulmonary hypertension using standard chest X ray. Sci Rep. 2020;10:19311.","journal-title":"Sci Rep"},{"key":"1019_CR31","doi-asserted-by":"publisher","first-page":"574","DOI":"10.1056\/NEJMoa010641","volume":"345","author":"MH Drazner","year":"2001","unstructured":"Drazner MH, Rame JE, Stevenson LW, et al. Prognostic importance of elevated jugular venous pressure and a third heart sound in patients with heart failure. N Engl J Med. 2001;345:574\u201381.","journal-title":"N Engl J Med"},{"key":"1019_CR32","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1002\/ejhf.1369","volume":"21","author":"W Mullens","year":"2019","unstructured":"Mullens W, Damman K, Harjola VP, et al. The use of diuretics in heart failure with congestion\u2014a position statement from the Heart Failure Association of the European Society of Cardiology. Eur J Heart Fail. 2019;21:137\u201355.","journal-title":"Eur J Heart Fail"},{"key":"1019_CR33","doi-asserted-by":"publisher","first-page":"1198","DOI":"10.1016\/j.cjca.2021.02.007","volume":"37","author":"Y Hirata","year":"2021","unstructured":"Hirata Y, Kusunose K, Tsuji T, et al. Deep learning for detection of elevated pulmonary artery wedge pressure using standard chest X-ray. Can J Cardiol. 2021;37:1198\u2013206.","journal-title":"Can J Cardiol"},{"key":"1019_CR34","first-page":"1","volume":"9","author":"IM Baltruschat","year":"2018","unstructured":"Baltruschat IM, Nickisch H, Grass M, Knopp T, et al. Comparison of deep learning approaches for multi-label chest X-ray classification. Sci Rep. 2018;9:1\u201310.","journal-title":"Sci Rep"},{"key":"1019_CR35","doi-asserted-by":"crossref","unstructured":"Li Z, Wang C, Han M, et al.: Thoracic disease identification and localization with limited supervision. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2018, pp. 8290\u20138299.","DOI":"10.1109\/CVPR.2018.00865"},{"key":"1019_CR36","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1016\/j.patrec.2018.10.027","volume":"130","author":"Q Guan","year":"2020","unstructured":"Guan Q, Huang Y. Multi-label chest X-ray image classification via category-wise residual attention learning. Pattern Recognit Lett. 2020;130:259\u201366.","journal-title":"Pattern Recognit Lett"},{"key":"1019_CR37","doi-asserted-by":"publisher","first-page":"101811","DOI":"10.1016\/j.media.2020.101811","volume":"66","author":"H Chen","year":"2020","unstructured":"Chen H, Miao S, Xu D, et al. Deep hiearchical multi-label classification applied to chest X-ray abnormality taxonomies. Med Image Anal. 2020;66:101811.","journal-title":"Med Image Anal"},{"key":"1019_CR38","doi-asserted-by":"publisher","first-page":"101846","DOI":"10.1016\/j.media.2020.101846","volume":"67","author":"H Wang","year":"2021","unstructured":"Wang H, Wang S, Qin Z, et al. Triple attention learning for classification of 14 thoracic diseases using chest radiography. Med Image Anal. 2021;67:101846.","journal-title":"Med Image Anal"},{"key":"1019_CR39","unstructured":"Simonyan K, Zisserman A: very deep convolutional networks for large-scale image recognition. In: Third international conference on learning representations, ICLR 2015\u2014conference track proceedings. 2014:1\u201314."},{"key":"1019_CR40","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, et al.: deep residual learning for image recognition. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR). IEEE; 2016. p. 770\u20138.","DOI":"10.1109\/CVPR.2016.90"},{"key":"1019_CR41","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511810817","volume-title":"Matrix analysis","author":"RA Horn","year":"1985","unstructured":"Horn RA, Johnson CR. Matrix analysis. Cambridge: Cambridge University Press; 1985."},{"key":"1019_CR42","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T: U-net: convolutional networks for biomedical image segmentation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2015;9351:234\u201341.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"1019_CR43","doi-asserted-by":"publisher","first-page":"577","DOI":"10.1109\/TMI.2013.2290491","volume":"33","author":"S Candemir","year":"2014","unstructured":"Candemir S, Jaeger S, Palaniappan K, et al. Lung segmentation in chest radiographs using anatomical atlases with nonrigid registration. IEEE Trans Med Imaging. 2014;33:577\u201390.","journal-title":"IEEE Trans Med Imaging"},{"key":"1019_CR44","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1109\/TMI.2013.2284099","volume":"33","author":"S Jaeger","year":"2014","unstructured":"Jaeger S, Karargyris A, Candemir S, et al. Automatic tuberculosis screening using chest radiographs. IEEE Trans Med Imaging. 2014;33:233\u201345.","journal-title":"IEEE Trans Med Imaging"},{"key":"1019_CR45","doi-asserted-by":"publisher","first-page":"370","DOI":"10.6009\/jjrt.KJ00001356834","volume":"56","author":"J Shiraishi","year":"2000","unstructured":"Shiraishi J. Standard digital image database: chest lung nodules and non-nodules\u202f: the review at the time of one and half year periods past from starting distribution. Jpn J Radiol Technol. 2000;56:370\u20135.","journal-title":"Jpn J Radiol Technol"},{"key":"1019_CR46","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1016\/j.media.2005.02.002","volume":"10","author":"B van Ginneken","year":"2006","unstructured":"van Ginneken B, Stegmann MB, Loog M. Segmentation of anatomical structures in chest radiographs using supervised methods: a comparative study of a public database. Med Image Anal. 2006;10:19\u201340.","journal-title":"Med Image Anal"},{"key":"1019_CR47","doi-asserted-by":"publisher","first-page":"116873","DOI":"10.1016\/j.eswa.2022.116873","volume":"198","author":"T Peng","year":"2022","unstructured":"Peng T, Gu Y, Ye Z, Cheng X, Wang J. A-LugSeg: Automatic and explainability-guided multi-site lung detection in chest X-ray images. Expert Syst Appl. 2022;198:116873.","journal-title":"Expert Syst Appl"},{"key":"1019_CR48","doi-asserted-by":"publisher","first-page":"075006","DOI":"10.1088\/1361-6560\/ac5d74","volume":"67","author":"T Peng","year":"2022","unstructured":"Peng T, Wang C, Zhang Y, Wang J. H-SegNet: Hybrid segmentation network for lung segmentation in chest radiographs using mask region-based convolutional neural network and adaptive closed polyline searching method. Phys Med Biol. 2022;67:075006.","journal-title":"Phys Med Biol"},{"key":"1019_CR49","doi-asserted-by":"publisher","first-page":"1107","DOI":"10.1093\/comjnl\/bxaa148","volume":"65","author":"T Peng","year":"2022","unstructured":"Peng T, Xu TC, Wang Y, Li F. Deep belief network and closed polygonal line for lung segmentation in chest radiographs. Comput J. 2022;65:1107\u201328.","journal-title":"Comput J"},{"key":"1019_CR50","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.compmedimag.2019.04.005","volume":"75","author":"SA Taghanaki","year":"2019","unstructured":"Taghanaki SA, Zheng Y, Kevin Zhou S, et al. Combo loss: Handling input and output imbalance in multi-organ segmentation. Comput Med Imaging Graph. 2019;75:24\u201333.","journal-title":"Comput Med Imaging Graph"},{"key":"1019_CR51","unstructured":"Han J, Kamber M, Pei J: Data mining. Concepts and techniques, 3rd (The Morgan Kaufmann Series in Data Management Systems). 2011."},{"key":"1019_CR52","doi-asserted-by":"publisher","first-page":"106947","DOI":"10.1016\/j.cmpb.2022.106947","volume":"222","author":"TB Chandra","year":"2022","unstructured":"Chandra TB, Singh BK, Jain D. Disease localization and severity assessment in chest X-ray images using multi-stage superpixels classification. Comput Methods Programs Biomed. 2022;222:106947.","journal-title":"Comput Methods Programs Biomed"},{"key":"1019_CR53","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, van der Maaten L, et al.: densely connected convolutional networks. In: Proceedings\u201430th IEEE conference on computer vision and pattern recognition, CVPR 2017. 2016; 2261\u20139.","DOI":"10.1109\/CVPR.2017.243"},{"key":"1019_CR54","doi-asserted-by":"crossref","unstructured":"Deng J, Dong W, Socher R, et al.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition. IEEE; 2009. p. 248\u201355.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"1019_CR55","unstructured":"Kingma DP, Ba J: Adam: a method for stochastic optimization. arXiv:1412.6980v9."},{"key":"1019_CR56","doi-asserted-by":"publisher","first-page":"1173","DOI":"10.1109\/TBME.2021.3117407","volume":"69","author":"H Guan","year":"2021","unstructured":"Guan H, Liu M. domain adaptation for medical image analysis: a survey. IEEE Trans Biomed Eng. 2021;69:1173\u201385.","journal-title":"IEEE Trans Biomed Eng"},{"key":"1019_CR57","doi-asserted-by":"crossref","unstructured":"Yan W, Wang Y, Gu S, et al.: The domain shift problem of medical image segmentation and vendor-adaptation by Unet-GAN. In Proc. Int. Conf. Med. Image Comput. Comput.- Assist. Intervention 2019, pp 623\u2013631.","DOI":"10.1007\/978-3-030-32245-8_69"}],"container-title":["BMC Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-023-01019-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12880-023-01019-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-023-01019-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,10]],"date-time":"2023-05-10T07:41:57Z","timestamp":1683704517000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedimaging.biomedcentral.com\/articles\/10.1186\/s12880-023-01019-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,9]]},"references-count":57,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["1019"],"URL":"https:\/\/doi.org\/10.1186\/s12880-023-01019-0","relation":{},"ISSN":["1471-2342"],"issn-type":[{"value":"1471-2342","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,9]]},"assertion":[{"value":"23 August 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 May 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 May 2023","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 current study was approved by the Teikyo University Medical Research Ethics Committee (no. 17-108-6, no.19-133) and the Tokushima University Hospital Review Board (no. 3217-3). Use of the Teikyo dataset for this study was approved by the Institutional Ethics Review Board (Teikyo University Review Board 17-108-6). All necessity for written informed consent from patients was waived by the Teikyo University Medical Research Ethics Committee (no. 17-108-6, no.19-133) and the Tokushima University Hospital Review Board (no. 3217-3), as long as patient data remained anonymous. The Tokushima dataset in this study was approved by the Institutional Ethics Review Board (Tokushima University Hospital Review Board 3217-3). All procedures were conducted in accordance with the Declaration of Helsinki.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interest.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"62"}}