{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T16:40:34Z","timestamp":1781109634859,"version":"3.54.1"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2021,1,25]],"date-time":"2021-01-25T00:00:00Z","timestamp":1611532800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,1,25]],"date-time":"2021-01-25T00:00:00Z","timestamp":1611532800000},"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":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2021,2]]},"DOI":"10.1007\/s11517-021-02321-1","type":"journal-article","created":{"date-parts":[[2021,1,25]],"date-time":"2021-01-25T08:05:19Z","timestamp":1611561919000},"page":"401-415","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":129,"title":["Feasibility study to improve deep learning in OCT diagnosis of rare retinal diseases with few-shot classification"],"prefix":"10.1007","volume":"59","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0890-8614","authenticated-orcid":false,"given":"Tae Keun","family":"Yoo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joon Yul","family":"Choi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong Kyu","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,1,25]]},"reference":[{"key":"2321_CR1","doi-asserted-by":"publisher","first-page":"2039","DOI":"10.1016\/S0140-6736(08)60872-7","volume":"371","author":"A Schieppati","year":"2008","unstructured":"Schieppati A, Henter J-I, Daina E, Aperia A (2008) Why rare diseases are an important medical and social issue. Lancet Lond Engl 371:2039\u20132041. https:\/\/doi.org\/10.1016\/S0140-6736(08)60872-7","journal-title":"Lancet Lond Engl"},{"key":"2321_CR2","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1186\/s13023-019-1040-6","volume":"14","author":"S Ronicke","year":"2019","unstructured":"Ronicke S, Hirsch MC, T\u00fcrk E, Larionov K, Tientcheu D, Wagner AD (2019) Can a decision support system accelerate rare disease diagnosis? Evaluating the potential impact of Ada DX in a retrospective study. Orphanet J Rare Dis 14:69. https:\/\/doi.org\/10.1186\/s13023-019-1040-6","journal-title":"Orphanet J Rare Dis"},{"key":"2321_CR3","doi-asserted-by":"publisher","first-page":"5200","DOI":"10.1167\/iovs.16-19964","volume":"57","author":"MD Abr\u00e0moff","year":"2016","unstructured":"Abr\u00e0moff MD, Lou Y, Erginay A, Clarida W, Amelon R, Folk JC, Niemeijer M (2016) Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning. Invest Ophthalmol Vis Sci 57:5200\u20135206. https:\/\/doi.org\/10.1167\/iovs.16-19964","journal-title":"Invest Ophthalmol Vis Sci"},{"key":"2321_CR4","doi-asserted-by":"publisher","unstructured":"Shah M, Ledo AR, Rittscher J (2020) Automated classification of normal and Stargardt disease optical coherence tomography images using deep learning. Acta Ophthalmol (Copenh) n\/a. https:\/\/doi.org\/10.1111\/aos.14353","DOI":"10.1111\/aos.14353"},{"key":"2321_CR5","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1167\/tvst.9.2.17","volume":"9","author":"MS Islam","year":"2020","unstructured":"Islam MS, Wang J-K, Johnson SS, Thurtell MJ, Kardon RH, Garvin MK (2020) A deep-learning approach for automated OCT en-face retinal vessel segmentation in cases of optic disc swelling using multiple en-face images as input. Transl Vis Sci Technol 9:17\u201317. https:\/\/doi.org\/10.1167\/tvst.9.2.17","journal-title":"Transl Vis Sci Technol"},{"key":"2321_CR6","doi-asserted-by":"publisher","unstructured":"Kermany DS, Goldbaum M, Cai W, et al (2018) Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172:1122-1131.e9. https:\/\/doi.org\/10.1016\/j.cell.2018.02.010","DOI":"10.1016\/j.cell.2018.02.010"},{"key":"2321_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/02713683.2016.1175019","volume":"42","author":"M Caixinha","year":"2017","unstructured":"Caixinha M, Nunes S (2017) Machine learning techniques in clinical vision sciences. Curr Eye Res 42:1\u201315. https:\/\/doi.org\/10.1080\/02713683.2016.1175019","journal-title":"Curr Eye Res"},{"key":"2321_CR8","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1038\/s41746-019-0135-8","volume":"2","author":"TK Yoo","year":"2019","unstructured":"Yoo TK, Ryu IH, Lee G, Kim Y, Kim JK, Lee IS, Kim JS, Rim TH (2019) Adopting machine learning to automatically identify candidate patients for corneal refractive surgery. Npj Digit Med 2:59. https:\/\/doi.org\/10.1038\/s41746-019-0135-8","journal-title":"Npj Digit Med"},{"key":"2321_CR9","doi-asserted-by":"publisher","first-page":"2402","DOI":"10.1001\/jama.2016.17216","volume":"316","author":"V Gulshan","year":"2016","unstructured":"Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, Venugopalan S, Widner K, Madams T, Cuadros J, Kim R, Raman R, Nelson PC, Mega JL, Webster DR (2016) Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316:2402\u20132410. https:\/\/doi.org\/10.1001\/jama.2016.17216","journal-title":"JAMA"},{"key":"2321_CR10","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0187336","volume":"12","author":"JY Choi","year":"2017","unstructured":"Choi JY, Yoo TK, Seo JG, Kwak J, Um TT, Rim TH (2017) Multi-categorical deep learning neural network to classify retinal images: a pilot study employing small database. PLoS One 12:e0187336. https:\/\/doi.org\/10.1371\/journal.pone.0187336","journal-title":"PLoS One"},{"key":"2321_CR11","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.compag.2018.08.013","volume":"153","author":"JGA Barbedo","year":"2018","unstructured":"Barbedo JGA (2018) Impact of dataset size and variety on the effectiveness of deep learning and transfer learning for plant disease classification. Comput Electron Agric 153:46\u201353. https:\/\/doi.org\/10.1016\/j.compag.2018.08.013","journal-title":"Comput Electron Agric"},{"key":"2321_CR12","doi-asserted-by":"publisher","first-page":"101660","DOI":"10.1016\/j.media.2020.101660","volume":"61","author":"G Quellec","year":"2020","unstructured":"Quellec G, Lamard M, Conze P-H, Massin P, Cochener B (2020) Automatic detection of rare pathologies in fundus photographs using few-shot learning. Med Image Anal 61:101660. https:\/\/doi.org\/10.1016\/j.media.2020.101660","journal-title":"Med Image Anal"},{"key":"2321_CR13","doi-asserted-by":"publisher","first-page":"112782","DOI":"10.1016\/j.eswa.2019.06.070","volume":"138","author":"S Feng","year":"2019","unstructured":"Feng S, Duarte MF (2019) Few-shot learning-based human activity recognition. Expert Syst Appl 138:112782. https:\/\/doi.org\/10.1016\/j.eswa.2019.06.070","journal-title":"Expert Syst Appl"},{"key":"2321_CR14","first-page":"2365","volume-title":"Metagan: an adversarial approach to few-shot learning","author":"R Zhang","year":"2018","unstructured":"Zhang R, Che T, Ghahramani Z et al (2018) Metagan: an adversarial approach to few-shot learning. Advances in Neural Information Processing Systems, In, pp 2365\u20132374"},{"key":"2321_CR15","unstructured":"Prabhu V, Kannan A, Ravuri M, et al (2018) Prototypical clustering networks for dermatological disease diagnosis. ArXiv181103066 Cs"},{"key":"2321_CR16","doi-asserted-by":"publisher","first-page":"105542","DOI":"10.1016\/j.compag.2020.105542","volume":"175","author":"D Arg\u00fceso","year":"2020","unstructured":"Arg\u00fceso D, Picon A, Irusta U, Medela A, San-Emeterio MG, Bereciartua A, Alvarez-Gila A (2020) Few-shot learning approach for plant disease classification using images taken in the field. Comput Electron Agric 175:105542. https:\/\/doi.org\/10.1016\/j.compag.2020.105542","journal-title":"Comput Electron Agric"},{"key":"2321_CR17","doi-asserted-by":"crossref","unstructured":"Mahajan K, Sharma M, Vig L (2020) Meta-DermDiagnosis: few-shot skin disease identification using meta-learning. Pp 730\u2013731","DOI":"10.1109\/CVPRW50498.2020.00373"},{"key":"2321_CR18","doi-asserted-by":"publisher","first-page":"1070","DOI":"10.1001\/jamaophthalmol.2020.3269","volume":"138","author":"P Burlina","year":"2020","unstructured":"Burlina P, Paul W, Mathew P, Joshi N, Pacheco KD, Bressler NM (2020) Low-shot deep learning of diabetic retinopathy with potential applications to address artificial intelligence bias in retinal diagnostics and rare ophthalmic diseases. JAMA Ophthalmol 138:1070\u20131077. https:\/\/doi.org\/10.1001\/jamaophthalmol.2020.3269","journal-title":"JAMA Ophthalmol"},{"key":"2321_CR19","doi-asserted-by":"publisher","first-page":"105828","DOI":"10.1016\/j.compag.2020.105828","volume":"179","author":"F Zhong","year":"2020","unstructured":"Zhong F, Chen Z, Zhang Y, Xia F (2020) Zero- and few-shot learning for diseases recognition of Citrus aurantium L. using conditional adversarial autoencoders. Comput Electron Agric 179:105828. https:\/\/doi.org\/10.1016\/j.compag.2020.105828","journal-title":"Comput Electron Agric"},{"key":"2321_CR20","doi-asserted-by":"publisher","first-page":"103980","DOI":"10.1016\/j.compbiomed.2020.103980","volume":"125","author":"TK Yoo","year":"2020","unstructured":"Yoo TK, Choi JY, Jang Y, Oh E, Ryu IH (2020) Toward automated severe pharyngitis detection with smartphone camera using deep learning networks. Comput Biol Med 125:103980. https:\/\/doi.org\/10.1016\/j.compbiomed.2020.103980","journal-title":"Comput Biol Med"},{"key":"2321_CR21","doi-asserted-by":"publisher","first-page":"194158","DOI":"10.1109\/ACCESS.2020.3033069","volume":"8","author":"Y Lai","year":"2020","unstructured":"Lai Y, Li G, Wu D, Lian W, Li C, Tian J, Ma X, Chen H, Xu W, Wei J, Zhang Y, Jiang G (2020) 2019 Novel coronavirus-infected pneumonia on CT: a feasibility study of few-shot learning for computerized diagnosis of emergency diseases. IEEE Access 8:194158\u2013194165. https:\/\/doi.org\/10.1109\/ACCESS.2020.3033069","journal-title":"IEEE Access"},{"key":"2321_CR22","doi-asserted-by":"publisher","first-page":"1334","DOI":"10.1001\/jamaophthalmol.2014.2854","volume":"132","author":"R Varma","year":"2014","unstructured":"Varma R, Bressler NM, Doan QV, Gleeson M, Danese M, Bower JK, Selvin E, Dolan C, Fine J, Colman S, Turpcu A (2014) Prevalence of and risk factors for diabetic macular edema in the United States. JAMA Ophthalmol 132:1334\u20131340. https:\/\/doi.org\/10.1001\/jamaophthalmol.2014.2854","journal-title":"JAMA Ophthalmol"},{"key":"2321_CR23","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1001\/archopht.121.3.379","volume":"121","author":"F Jonasson","year":"2003","unstructured":"Jonasson F, Arnarsson A, Sasaki H, Peto T, Sasaki K, Bird AC (2003) The prevalence of age-related maculopathy in Iceland: Reykjavik eye study. Arch Ophthalmol Chic Ill 1960 121:379\u2013385. https:\/\/doi.org\/10.1001\/archopht.121.3.379","journal-title":"Arch Ophthalmol Chic Ill"},{"key":"2321_CR24","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1016\/j.bj.2016.04.003","volume":"39","author":"RK Murthy","year":"2016","unstructured":"Murthy RK, Haji S, Sambhav K, Grover S, Chalam KV (2016) Clinical applications of spectral domain optical coherence tomography in retinal diseases. Biom J 39:107\u2013120. https:\/\/doi.org\/10.1016\/j.bj.2016.04.003","journal-title":"Biom J"},{"key":"2321_CR25","doi-asserted-by":"publisher","first-page":"1164","DOI":"10.1001\/jamaophthalmol.2018.3293","volume":"136","author":"TH Rim","year":"2018","unstructured":"Rim TH, Kim HS, Kwak J, Lee JS, Kim DW, Kim SS (2018) Association of corticosteroid use with incidence of central serous chorioretinopathy in South Korea. JAMA Ophthalmol 136:1164\u20131169. https:\/\/doi.org\/10.1001\/jamaophthalmol.2018.3293","journal-title":"JAMA Ophthalmol"},{"key":"2321_CR26","doi-asserted-by":"publisher","unstructured":"Bitner H, Schatz P, Mizrahi-Meissonnier L, et al (2012) Frequency, genotype, and clinical spectrum of best vitelliform macular dystrophy: data from a National Center in Denmark. Am J Ophthalmol 154:403-412.e4. https:\/\/doi.org\/10.1016\/j.ajo.2012.02.036","DOI":"10.1016\/j.ajo.2012.02.036"},{"key":"2321_CR27","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1080\/09286580802105814","volume":"15","author":"P Sen","year":"2008","unstructured":"Sen P, Bhargava A, George R, Ramesh SV, Hemamalini A, Prema R, Kumaramanickavel G, Vijaya L (2008) Prevalence of retinitis pigmentosa in south Indian population aged above 40 years. Ophthalmic Epidemiol 15:279\u2013281. https:\/\/doi.org\/10.1080\/09286580802105814","journal-title":"Ophthalmic Epidemiol"},{"key":"2321_CR28","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1038\/s41431-019-0508-0","volume":"28","author":"S Nguengang Wakap","year":"2020","unstructured":"Nguengang Wakap S, Lambert DM, Olry A, Rodwell C, Gueydan C, Lanneau V, Murphy D, le Cam Y, Rath A (2020) Estimating cumulative point prevalence of rare diseases: analysis of the Orphanet database. Eur J Hum Genet 28:165\u2013173. https:\/\/doi.org\/10.1038\/s41431-019-0508-0","journal-title":"Eur J Hum Genet"},{"key":"2321_CR29","doi-asserted-by":"publisher","first-page":"473","DOI":"10.1097\/IAE.0b013e3181bd2c71","volume":"30","author":"KZ Aung","year":"2010","unstructured":"Aung KZ, Wickremasinghe SS, Makeyeva G et al (2010) The prevalence estimates of macular telangiectasia type 2: the Melbourne collaborative cohort study. RETINA 30:473\u2013478. https:\/\/doi.org\/10.1097\/IAE.0b013e3181bd2c71","journal-title":"RETINA"},{"key":"2321_CR30","doi-asserted-by":"crossref","first-page":"5221","DOI":"10.1167\/iovs.12-9702","volume":"53","author":"RE Williams","year":"2012","unstructured":"Williams RE, Beeby M, Logie J (2012) Prevalence of diagnosed macular hole, macular pucker, vitreomacular adhesions\/traction, retinal tear\/detachment, and pterygium in US health care claims databases. Invest Ophthalmol Vis Sci 53:5221\u20135221","journal-title":"Invest Ophthalmol Vis Sci"},{"key":"2321_CR31","unstructured":"Wang Y, Yao Q (2019) Few-shot learning: a survey. ArXiv Prepr ArXiv190405046"},{"key":"2321_CR32","first-page":"3359","volume-title":"Joint pose and expression modeling for facial expression recognition","author":"F Zhang","year":"2018","unstructured":"Zhang F, Zhang T, Mao Q, Xu C (2018) Joint pose and expression modeling for facial expression recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, In, pp 3359\u20133368"},{"key":"2321_CR33","first-page":"11937","volume-title":"Improving few-shot user-specific gaze adaptation via gaze redirection synthesis","author":"Y Yu","year":"2019","unstructured":"Yu Y, Liu G, Odobez J-M (2019) Improving few-shot user-specific gaze adaptation via gaze redirection synthesis. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, In, pp 11937\u201311946"},{"key":"2321_CR34","first-page":"10551","volume-title":"Few-shot unsupervised image-to-image translation","author":"M-Y Liu","year":"2019","unstructured":"Liu M-Y, Huang X, Mallya A et al (2019) Few-shot unsupervised image-to-image translation. Proceedings of the IEEE International Conference on Computer Vision, In, pp 10551\u201310560"},{"key":"2321_CR35","doi-asserted-by":"publisher","first-page":"1342","DOI":"10.1038\/s41591-018-0107-6","volume":"24","author":"J De Fauw","year":"2018","unstructured":"De Fauw J, Ledsam JR, Romera-Paredes B et al (2018) Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat Med 24:1342\u20131350. https:\/\/doi.org\/10.1038\/s41591-018-0107-6","journal-title":"Nat Med"},{"key":"2321_CR36","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1167\/tvst.9.2.8","volume":"9","author":"TK Yoo","year":"2020","unstructured":"Yoo TK, Ryu IH, Choi H, Kim JK, Lee IS, Kim JS, Lee G, Rim TH (2020) Explainable machine learning approach as a tool to understand factors used to select the refractive surgery technique on the expert level. Transl Vis Sci Technol 9:8\u20138","journal-title":"Transl Vis Sci Technol"},{"key":"2321_CR37","doi-asserted-by":"publisher","unstructured":"Figueroa-Mata G, Mata-Montero E (2020) Using a convolutional Siamese Network for image-based plant species identification with small datasets Biomim Basel Switz 5. https:\/\/doi.org\/10.3390\/biomimetics5010008","DOI":"10.3390\/biomimetics5010008"},{"key":"2321_CR38","doi-asserted-by":"publisher","first-page":"677","DOI":"10.1007\/s11517-018-1915-z","volume":"57","author":"TK Yoo","year":"2019","unstructured":"Yoo TK, Choi JY, Seo JG, Ramasubramanian B, Selvaperumal S, Kim DW (2019) The possibility of the combination of OCT and fundus images for improving the diagnostic accuracy of deep learning for age-related macular degeneration: a preliminary experiment. Med Biol Eng Comput 57:677\u2013687. https:\/\/doi.org\/10.1007\/s11517-018-1915-z","journal-title":"Med Biol Eng Comput"},{"key":"2321_CR39","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1016\/j.compbiolchem.2004.09.006","volume":"28","author":"J Gorodkin","year":"2004","unstructured":"Gorodkin J (2004) Comparing two K-category assignments by a K-category correlation coefficient. Comput Biol Chem 28:367\u2013374. https:\/\/doi.org\/10.1016\/j.compbiolchem.2004.09.006","journal-title":"Comput Biol Chem"},{"key":"2321_CR40","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1167\/tvst.9.2.11","volume":"9","author":"RT Yanagihara","year":"2020","unstructured":"Yanagihara RT, Lee CS, Ting DSW, Lee AY (2020) Methodological challenges of deep learning in optical coherence tomography for retinal diseases: a review. Transl Vis Sci Technol 9:11\u201311. https:\/\/doi.org\/10.1167\/tvst.9.2.11","journal-title":"Transl Vis Sci Technol"},{"key":"2321_CR41","doi-asserted-by":"publisher","DOI":"10.7717\/peerj.5696","volume":"6","author":"T Nagasawa","year":"2018","unstructured":"Nagasawa T, Tabuchi H, Masumoto H, Enno H, Niki M, Ohsugi H, Mitamura Y (2018) Accuracy of deep learning, a machine learning technology, using ultra-wide-field fundus ophthalmoscopy for detecting idiopathic macular holes. PeerJ 6:e5696. https:\/\/doi.org\/10.7717\/peerj.5696","journal-title":"PeerJ"},{"key":"2321_CR42","doi-asserted-by":"publisher","DOI":"10.7717\/peerj.6900","volume":"7","author":"H Masumoto","year":"2019","unstructured":"Masumoto H, Tabuchi H, Nakakura S, Ohsugi H, Enno H, Ishitobi N, Ohsugi E, Mitamura Y (2019) Accuracy of a deep convolutional neural network in detection of retinitis pigmentosa on ultrawide-field images. PeerJ 7:e6900. https:\/\/doi.org\/10.7717\/peerj.6900","journal-title":"PeerJ"},{"key":"2321_CR43","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1167\/tvst.9.2.15","volume":"9","author":"Y-Z Wang","year":"2020","unstructured":"Wang Y-Z, Galles D, Klein M, Locke KG, Birch DG (2020) Application of a deep machine learning model for automatic measurement of EZ width in SD-OCT images of RP. Transl Vis Sci Technol 9:15\u201315. https:\/\/doi.org\/10.1167\/tvst.9.2.15","journal-title":"Transl Vis Sci Technol"},{"key":"2321_CR44","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1007\/s00521-015-2059-9","volume":"28","author":"R Arunkumar","year":"2017","unstructured":"Arunkumar R, Karthigaikumar P (2017) Multi-retinal disease classification by reduced deep learning features. Neural Comput Appl 28:329\u2013334. https:\/\/doi.org\/10.1007\/s00521-015-2059-9","journal-title":"Neural Comput Appl"},{"key":"2321_CR45","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1167\/tvst.7.6.41","volume":"7","author":"W Lu","year":"2018","unstructured":"Lu W, Tong Y, Yu Y, Xing Y, Chen C, Shen Y (2018) Deep learning-based automated classification of multi-categorical abnormalities from optical coherence tomography images. Transl Vis Sci Technol 7:41\u201341. https:\/\/doi.org\/10.1167\/tvst.7.6.41","journal-title":"Transl Vis Sci Technol"},{"key":"2321_CR46","doi-asserted-by":"crossref","unstructured":"Xian Y, Sharma S, Schiele B, Akata Z (2019) F-VAEGAN-D2: a feature generating framework for any-shot learning. Pp 10275\u201310284","DOI":"10.1109\/CVPR.2019.01052"},{"key":"2321_CR47","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1016\/j.neucom.2018.09.013","volume":"321","author":"M Frid-Adar","year":"2018","unstructured":"Frid-Adar M, Diamant I, Klang E, Amitai M, Goldberger J, Greenspan H (2018) GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification. Neurocomputing 321:321\u2013331. https:\/\/doi.org\/10.1016\/j.neucom.2018.09.013","journal-title":"Neurocomputing"},{"key":"2321_CR48","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1007\/978-981-13-8950-4_27","volume-title":"Neural approaches to dynamics of signal exchanges","author":"C Han","year":"2020","unstructured":"Han C, Rundo L, Araki R, Furukawa Y, Mauri G, Nakayama H, Hayashi H (2020) Infinite brain MR images: PGGAN-based data augmentation for tumor detection. In: Esposito A, Faundez-Zanuy M, Morabito FC, Pasero E (eds) Neural approaches to dynamics of signal exchanges. Springer, Singapore, pp 291\u2013303"},{"key":"2321_CR49","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1016\/j.media.2019.06.014","volume":"57","author":"M Rubin","year":"2019","unstructured":"Rubin M, Stein O, Turko NA, Nygate Y, Roitshtain D, Karako L, Barnea I, Giryes R, Shaked NT (2019) TOP-GAN: stain-free cancer cell classification using deep learning with a small training set. Med Image Anal 57:176\u2013185. https:\/\/doi.org\/10.1016\/j.media.2019.06.014","journal-title":"Med Image Anal"},{"key":"2321_CR50","doi-asserted-by":"publisher","first-page":"103698","DOI":"10.1016\/j.compbiomed.2020.103698","volume":"119","author":"C Muramatsu","year":"2020","unstructured":"Muramatsu C, Nishio M, Goto T, Oiwa M, Morita T, Yakami M, Kubo T, Togashi K, Fujita H (2020) Improving breast mass classification by shared data with domain transformation using a generative adversarial network. Comput Biol Med 119:103698. https:\/\/doi.org\/10.1016\/j.compbiomed.2020.103698","journal-title":"Comput Biol Med"},{"key":"2321_CR51","doi-asserted-by":"publisher","first-page":"16884","DOI":"10.1038\/s41598-019-52737-x","volume":"9","author":"V Sandfort","year":"2019","unstructured":"Sandfort V, Yan K, Pickhardt PJ, Summers RM (2019) Data augmentation using generative adversarial networks (CycleGAN) to improve generalizability in CT segmentation tasks. Sci Rep 9:16884. https:\/\/doi.org\/10.1038\/s41598-019-52737-x","journal-title":"Sci Rep"},{"key":"2321_CR52","doi-asserted-by":"publisher","first-page":"103628","DOI":"10.1016\/j.compbiomed.2020.103628","volume":"103628","author":"TK Yoo","year":"2020","unstructured":"Yoo TK, Choi JY, Kim HK (2020) A generative adversarial network approach to predicting postoperative appearance after orbital decompression surgery for thyroid eye disease. Comput Biol Med 103628:103628. https:\/\/doi.org\/10.1016\/j.compbiomed.2020.103628","journal-title":"Comput Biol Med"},{"key":"2321_CR53","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41746-020-0247-1","volume":"3","author":"MYT Yip","year":"2020","unstructured":"Yip MYT, Lim G, Lim ZW, Nguyen QD, Chong CCY, Yu M, Bellemo V, Xie Y, Lee XQ, Hamzah H, Ho J, Tan TE, Sabanayagam C, Grzybowski A, Tan GSW, Hsu W, Lee ML, Wong TY, Ting DSW (2020) Technical and imaging factors influencing performance of deep learning systems for diabetic retinopathy. Npj Digit Med 3:1\u201312. https:\/\/doi.org\/10.1038\/s41746-020-0247-1","journal-title":"Npj Digit Med"},{"key":"2321_CR54","doi-asserted-by":"publisher","unstructured":"Han SS, Park I, Chang SE, et al (2020) Augmented intelligence dermatology: deep neural networks empower medical professionals in diagnosing skin cancer and predicting treatment options for 134 skin disorders. J Invest Dermatol 0: https:\/\/doi.org\/10.1016\/j.jid.2020.01.019","DOI":"10.1016\/j.jid.2020.01.019"}],"container-title":["Medical & Biological Engineering & Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-021-02321-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-021-02321-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-021-02321-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T06:19:15Z","timestamp":1744179555000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-021-02321-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,25]]},"references-count":54,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2021,2]]}},"alternative-id":["2321"],"URL":"https:\/\/doi.org\/10.1007\/s11517-021-02321-1","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,25]]},"assertion":[{"value":"15 April 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 January 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 January 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All procedures were performed in accordance with the ethical standards of the institutional and\/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. This article does not contain any studies with human participants performed by any of the authors. This study did not require ethics committee approval; this is because the researchers used open web-based and deidentified data.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"The authors declare that they have no conflict of interest.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}