{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,29]],"date-time":"2026-03-29T07:07:45Z","timestamp":1774768065003,"version":"3.50.1"},"reference-count":50,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2020]]},"DOI":"10.1109\/access.2020.3016653","type":"journal-article","created":{"date-parts":[[2020,8,14]],"date-time":"2020-08-14T20:25:07Z","timestamp":1597436707000},"page":"150725-150737","source":"Crossref","is-referenced-by-count":81,"title":["Improving Skin-Disease Classification Based on Customized Loss Function Combined With Balanced Mini-Batch Logic and Real-Time Image Augmentation"],"prefix":"10.1109","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5507-6454","authenticated-orcid":false,"given":"Tri-Cong","family":"Pham","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6160-3356","authenticated-orcid":false,"given":"Antoine","family":"Doucet","sequence":"additional","affiliation":[]},{"given":"Chi-Mai","family":"Luong","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1572-7248","authenticated-orcid":false,"given":"Cong-Thanh","family":"Tran","sequence":"additional","affiliation":[]},{"given":"Van-Dung","family":"Hoang","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/ICMLA.2019.00134"},{"key":"ref38","article-title":"Cost-aware pre-training for multiclass cost-sensitive deep learning","author":"chung","year":"2015","journal-title":"arXiv 1511 09337"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.2016.7533053"},{"key":"ref32","article-title":"The impact of imbalanced training data for convolutional neural networks","author":"hensman","year":"2015"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/72.286891"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1038\/323533a0"},{"key":"ref37","doi-asserted-by":"crossref","first-page":"3573","DOI":"10.1109\/TNNLS.2017.2732482","article-title":"Cost-sensitive learning of deep feature representations from imbalanced data","volume":"29","author":"khan","year":"2018","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.112918"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2858826"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2016.7727770"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejca.2019.02.005"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1016\/j.jaad.2019.07.016"},{"key":"ref29","author":"codella","year":"2020","journal-title":"ISIC 2018 Skin Lesion Analysis Towards Melanoma Detection"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-05070-2_6"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(18)31559-9"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/JAS.2019.1911447"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2019.2915839"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2017.2755595"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2018.2846646"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/JAS.2019.1911774"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejca.2019.04.001"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1016\/j.jaad.2017.08.016"},{"key":"ref50","article-title":"BCN20000: Dermoscopic lesions in the wild","author":"combalia","year":"2019","journal-title":"arXiv 1908 02288"},{"key":"ref10","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"lecun","year":"2015","journal-title":"Nature"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/JAS.2019.1911393"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2017.58"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/JAS.2019.1911825"},{"key":"ref13","article-title":"AI outperformed every dermatologist: Improved dermoscopic melanoma diagnosis through customizing batch logic and loss function in an optimized deep CNN architecture","author":"tri pham","year":"2020","journal-title":"arXiv 2003 02597"},{"key":"ref14","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1038\/nature21056","article-title":"Dermatologist-level classification of skin cancer with deep neural networks","volume":"542","author":"esteva","year":"2017","journal-title":"Nature"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-75420-8_54"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1111\/exd.13777"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1001\/jamadermatol.2018.4378"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejca.2018.12.016"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1093\/annonc\/mdy166"},{"key":"ref4","author":"codella","year":"2019","journal-title":"Skin Lesion Analysis Towards Melanoma Detection"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-05070-2_50"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1111\/ajd.12758"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1046\/j.1365-2133.2003.05023.x"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/JSYST.2013.2271540"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2007.01.003"},{"key":"ref49","article-title":"Skin lesion analysis toward melanoma detection: A challenge at the 2017 international symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC)","author":"codella","year":"2017","journal-title":"arXiv 1710 05006"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/ICSSE.2019.8823124"},{"key":"ref46","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"srivastava","year":"2014","journal-title":"J Mach Learn Res"},{"key":"ref45","first-page":"448","article-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","volume":"1","author":"ioffe","year":"2015","journal-title":"Proc 32nd Int Conf Mach Learn"},{"key":"ref48","article-title":"The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions","volume":"5","author":"tschandl","year":"2018","journal-title":"Data Science Journal"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2985097"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref44","article-title":"EfficientNet: Rethinking model scaling for convolutional neural networks","author":"tan","year":"2019","journal-title":"arXiv 1905 11946"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/8948470\/09167198.pdf?arnumber=9167198","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,17]],"date-time":"2021-12-17T19:55:11Z","timestamp":1639770911000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9167198\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"references-count":50,"URL":"https:\/\/doi.org\/10.1109\/access.2020.3016653","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]}}}