{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T03:43:05Z","timestamp":1764906185458,"version":"3.37.3"},"reference-count":56,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2023]]},"DOI":"10.1109\/access.2023.3291610","type":"journal-article","created":{"date-parts":[[2023,7,3]],"date-time":"2023-07-03T18:20:53Z","timestamp":1688408453000},"page":"76437-76447","source":"Crossref","is-referenced-by-count":5,"title":["Ensembling to Leverage the Interpretability of Medical Image Analysis Systems"],"prefix":"10.1109","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-9258-5250","authenticated-orcid":false,"given":"Argyrios","family":"Zafeiriou","sequence":"first","affiliation":[{"name":"Department of Digital Systems, University of Piraeus, Piraeus, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9234-0069","authenticated-orcid":false,"given":"Athanasios","family":"Kallipolitis","sequence":"additional","affiliation":[{"name":"Department of Digital Systems, University of Piraeus, Piraeus, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2860-399X","authenticated-orcid":false,"given":"Ilias","family":"Maglogiannis","sequence":"additional","affiliation":[{"name":"Department of Digital Systems, University of Piraeus, Piraeus, Greece"}]}],"member":"263","reference":[{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.3390\/jimaging6060052"},{"article-title":"Ensembling to leverage the interpretability of medical image analysis systems","year":"2022","author":"zafeiriou","key":"ref12"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3184453"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejca.2022.02.025"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102470"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2016.2599820"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.6064"},{"key":"ref10","article-title":"Towards better understanding of gradient-based attribution methods for deep neural networks","author":"ancona","year":"2017","journal-title":"arXiv 1711 06104"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2020.105608"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3217217"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.01.048"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3255403"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.634"},{"key":"ref50","first-page":"1","article-title":"PyTorch: An imperative style, high-performance deep learning library","volume":"32","author":"paszke","year":"2019","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref46","first-page":"6105","article-title":"EfficientNet: Rethinking model scaling for convolutional neural networks","author":"tan","year":"2019","journal-title":"Proc Int Conf Mach Learn (ICML)"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106622"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW56347.2022.00309"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2019.2913372"},{"journal-title":"100 000 histological images of human colorectal cancer and healthy tissue","year":"2018","author":"kather","key":"ref42"},{"journal-title":"SIIM-ISIC-Melanoma-Classification-1st-Place-Solution","year":"2021","author":"ha","key":"ref41"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"journal-title":"Imagenette","year":"2022","author":"howard","key":"ref43"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2020.3004555"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0130140"},{"key":"ref7","first-page":"3145","article-title":"Learning important features through propagating activation differences","author":"shrikumar","year":"2017","journal-title":"Proc Int Conf Mach Learn (ICML)"},{"key":"ref9","first-page":"3319","article-title":"Axiomatic attribution for deep networks","author":"sundararajan","year":"2017","journal-title":"Proc Int Conf Mach Learn (ICML)"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.371"},{"key":"ref3","first-page":"818","article-title":"Visualizing and understanding convolutional networks","author":"zeiler","year":"2014","journal-title":"Proc Eur Conf Comput Vis"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.74"},{"key":"ref5","article-title":"Not just a black box: Learning important features through propagating activation differences","author":"shrikumar","year":"2016","journal-title":"arXiv 1605 01713"},{"journal-title":"Melanoma-Winning-Models","year":"2021","author":"howard","key":"ref40"},{"key":"ref35","first-page":"1","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":"ref34","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177729694"},{"key":"ref37","article-title":"BCN20000: Dermoscopic lesions in the wild","author":"combalia","year":"2019","journal-title":"arXiv 1908 02288"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2018.8363547"},{"key":"ref31","first-page":"1","article-title":"Sanity checks for saliency maps","volume":"31","author":"adebayo","year":"2018","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2013.150"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1287\/mnsc.6.4.366"},{"key":"ref32","first-page":"9046","article-title":"When explanations lie: Why many modified bp attributions fail","author":"sixt","year":"2020","journal-title":"Proc Int Conf Mach Learn (ICML)"},{"journal-title":"The Assessment List on Trustworthy Artificial Intelligence (ALTAI)","year":"2020","author":"ala-pietil\u00e4","key":"ref2"},{"journal-title":"Ethics guidelines for trustworthy ai","year":"2019","key":"ref1"},{"key":"ref39","article-title":"Identifying melanoma images using EfficientNet ensemble: Winning solution to the SIIM-ISIC Melanoma Classification challenge","author":"ha","year":"2020","journal-title":"arXiv 2010 05351"},{"key":"ref38","first-page":"1","article-title":"A patient-centric dataset of images and metadata for identifying melanomas using clinical context","volume":"8","author":"rotemberg","year":"2021","journal-title":"Data Science Journal"},{"key":"ref24","article-title":"RISE: Randomized input sampling for explanation of black-box models","author":"petsiuk","year":"2018","journal-title":"arXiv 1806 07421"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2018.00097"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.3390\/a14100278"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102364"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.2147\/OPTH.S312236"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3208957"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3232561"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/s00371-019-01750-2"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/s41095-019-0149-9"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2015.2487833"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/10005208\/10171246.pdf?arnumber=10171246","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,14]],"date-time":"2023-08-14T18:11:14Z","timestamp":1692036674000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10171246\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"references-count":56,"URL":"https:\/\/doi.org\/10.1109\/access.2023.3291610","relation":{},"ISSN":["2169-3536"],"issn-type":[{"type":"electronic","value":"2169-3536"}],"subject":[],"published":{"date-parts":[[2023]]}}}