{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,3]],"date-time":"2026-05-03T07:54:22Z","timestamp":1777794862018,"version":"3.51.4"},"reference-count":112,"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:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"National Funds"},{"name":"FCT - Foundation for Science and Technology","award":["CISUC - UID\/CEC\/00326\/2020"],"award-info":[{"award-number":["CISUC - UID\/CEC\/00326\/2020"]}]},{"DOI":"10.13039\/501100004895","name":"European Social Fund","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004895","id-type":"DOI","asserted-by":"publisher"}]},{"name":"FCT Research","award":["SFRH\/BD\/136786\/2018"],"award-info":[{"award-number":["SFRH\/BD\/136786\/2018"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Rev. Biomed. Eng."],"published-print":{"date-parts":[[2023]]},"DOI":"10.1109\/rbme.2021.3131358","type":"journal-article","created":{"date-parts":[[2021,11,30]],"date-time":"2021-11-30T23:44:52Z","timestamp":1638315892000},"page":"192-207","source":"Crossref","is-referenced-by-count":25,"title":["Interpreting Deep Machine Learning Models: An Easy Guide for Oncologists"],"prefix":"10.1109","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9477-0078","authenticated-orcid":false,"given":"Jose P.","family":"Amorim","sequence":"first","affiliation":[{"name":"Department of Informatics Engineering, University of Coimbra, CISUC, Coimbra, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9278-8194","authenticated-orcid":false,"given":"Pedro H.","family":"Abreu","sequence":"additional","affiliation":[{"name":"Department of Informatics Engineering, University of Coimbra, CISUC, Coimbra, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6480-8434","authenticated-orcid":false,"given":"Alberto","family":"Fernandez","sequence":"additional","affiliation":[{"name":"DaSCI Andalusian Research Institute, University of Granada, Granada, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2434-9990","authenticated-orcid":false,"given":"Mauricio","family":"Reyes","sequence":"additional","affiliation":[{"name":"Insel Data Science Center, Inselspital, Bern University Hospital, Bern, Switzerland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2465-5143","authenticated-orcid":false,"given":"Joao","family":"Santos","sequence":"additional","affiliation":[{"name":"IPO-Porto Research Center &#x2013; Portuguese Institute of Oncology of Porto, Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3940-0688","authenticated-orcid":false,"given":"Miguel H.","family":"Abreu","sequence":"additional","affiliation":[{"name":"Department of Medical Oncology, Portuguese Oncology Institute of Porto, Porto, Portugal"}]}],"member":"263","reference":[{"key":"ref1","article-title":"FDA approvals for smart algorithms in medicine in one giant infographic","volume-title":"Futurist","year":"2019"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1177\/0272989X9601600409"},{"key":"ref3","article-title":"The perceptron, a perceiving and recognizing automaton project para","author":"Rosenblatt","year":"1957"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejca.2019.02.005"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2017.14585"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1148\/ryai.2020190043"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.visinf.2017.01.006"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1038\/323533a0"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3029881"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref11","first-page":"1","article-title":"Deep learning of representations for unsupervised and transfer learning","volume-title":"Proc. J. Mach. Learn. Res.: Workshop Conf.","author":"Bengio","year":"2011"},{"issue":"4","key":"ref12","first-page":"582","article-title":"Deep learning techniques for medical image segmentation: Achievements and challenges","volume-title":"J. Digit. Imag.","volume":"32","author":"Hesamian","year":"2019"},{"issue":"11","key":"ref13","first-page":"3266","article-title":"Deep learning predicts lung cancer treatment response from serial medical imaging","volume-title":"Clin. Cancer Res.","volume":"25","author":"Xu","year":"2019"},{"key":"ref14","first-page":"37","article-title":"Autoencoders, unsupervised learning and deep architectures","volume-title":"Proc. Int. Conf. Unsupervised Transfer Learn. Workshop","author":"Baldi","year":"2011"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1142\/9789814644730_0014"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/3236009"},{"key":"ref17","article-title":"Towards A rigorous science of interpretable machine learning","author":"Doshi-Velez","year":"2017"},{"key":"ref18","article-title":"Interpretable machine learning","author":"Molnar","year":"2019"},{"key":"ref19","first-page":"1803","article-title":"How to explain individual classification decisions","volume":"11","author":"Baehrens","year":"2010","journal-title":"J. Mach. Learn. Res."},{"key":"ref20","article-title":"The Taylor decomposition: A unified generalization of the Oaxaca method to nonlinear models","author":"Bazen","year":"2013"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0130140"},{"key":"ref22","first-page":"1","article-title":"Deep inside convolutional networks: Visualising image classification models and saliency maps","volume-title":"Proc. Workshop Int. Conf. Learn. Representations","author":"Simonyan","year":"2014"},{"issue":"2","key":"ref23","first-page":"246","article-title":"Artificial intelligence for mammography and digital breast tomosynthesis: Current concepts and future perspectives","volume-title":"Radiology","volume":"293","author":"Geras","year":"2019"},{"key":"ref24","first-page":"1","article-title":"Understanding neural networks through deep visualization","volume-title":"Proc. Int. Conf. Mach. Learn. - Deep Learn. Workshop","author":"Yosinski","year":"2015"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.21105\/joss.00861"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1117\/12.2512584"},{"key":"ref27","first-page":"633","article-title":"Expert identification of visual primitives used by CNNs during mammogram classification","volume-title":"Proc. SPIE","author":"Wu","year":"2018"},{"key":"ref28","first-page":"3387","article-title":"Synthesizing the preferred inputs for neurons in neural networks via deep generator networks","volume-title":"Adv. Neural Inf. Process. Syst.","author":"Nguyen","year":"2016"},{"key":"ref29","doi-asserted-by":"crossref","DOI":"10.23915\/distill.00007","article-title":"Feature visualization","volume-title":"Distill","author":"Olah","year":"2017"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11491"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.101985"},{"key":"ref32","first-page":"212","article-title":"Case-based explanation of non-case-based learning methods","volume-title":"Proc. AMIA Symp.","author":"Caruana","year":"1999"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-019-04051-w"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2017.7797"},{"key":"ref35","first-page":"1","article-title":"Visual interpretability for patch-based classification of breast cancer histopathology images","volume-title":"Proc. Med. Imag. Deep Learn.","author":"Graziani","year":"2018"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-02628-8_14"},{"key":"ref37","article-title":"Concept attribution: Explaining CNN decisions to physicians","volume-title":"Comput. Biol. Med.","volume":"123","author":"Graziani","year":"2020"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/aaef0a"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1002\/mp.12453"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-33850-3_3"},{"key":"ref41","first-page":"7775","article-title":"Towards robust interpretability with self-explaining neural networks","volume-title":"Advances in Neural Information Processing Systems","volume":"31","author":"Alvarez-Melis","year":"2018"},{"key":"ref42","first-page":"373","article-title":"Interpreting deep learning models for ordinal problems","volume-title":"Proc. Eur. Symp. Artif. Neural Netw., Comput. Intell. Mach. Learn.","author":"Amorim","year":"2018"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1515\/jaiscr-2017-0019"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.3389\/fgene.2019.00166"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jproteome.7b00595"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.3390\/cancers11040494"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-02628-8_13"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1117\/12.2043872"},{"key":"ref49","article-title":"PatchNet: Interpretable neural networks for image classification","author":"Radhakrishnan","year":"2017"},{"key":"ref50","article-title":"Deep learning under the microscope: Improving the interpretability of medical imaging neural networks","author":"Paschali","year":"2019"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2018.2806962"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-40763-5_50"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-02628-8_16"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-02628-8_15"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2019.8852409"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-02628-8_11"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pmed.1002686"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2018.8489440"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.01.048"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1002\/mp.13497"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-02628-8_12"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2017.12.009"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1142\/9789813235533_0031"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-017-10649-8"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1717139115"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1109\/BIBM.2018.8621108"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-017-11817-6"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1145\/3307339.3342189"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.3390\/app9112183"},{"key":"ref70","first-page":"1","article-title":"Interpretable fully convolutional classification of intrapapillary capillary loops for real-time detection of early squamous neoplasia","author":"Garcia-Peraza-Herrera","year":"2018"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2017.114"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1039\/C6SC03738K"},{"issue":"11","key":"ref73","first-page":"1","article-title":"An interpretable and expandable deep learning diagnostic system for multiple ocular diseases: Qualitative study","volume-title":"J. Med. Internet Res.","volume":"20","author":"Zhang","year":"2018"},{"key":"ref74","first-page":"53223","article-title":"A novel interpretable computer-aided diagnosis system of thyroid nodules on ultrasound based on clinical experience","volume-title":"IEEE Access","volume":"8","author":"Zhang","year":"2020"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-019-0052-1"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00943"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.378"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-019-48995-4"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.74"},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.354"},{"key":"ref81","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-35280-8_309"},{"key":"ref82","first-page":"2668","article-title":"Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (TCAV)","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Kim","year":"2017"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.1117\/12.2512621"},{"key":"ref84","article-title":"H2O.ai","year":"2019"},{"key":"ref85","doi-asserted-by":"publisher","DOI":"10.1038\/nature21056"},{"key":"ref86","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.319"},{"key":"ref87","doi-asserted-by":"publisher","DOI":"10.1259\/bjro.20190021"},{"key":"ref88","doi-asserted-by":"publisher","DOI":"10.7312\/haza92762-003"},{"key":"ref89","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939778"},{"key":"ref90","doi-asserted-by":"publisher","DOI":"10.1007\/s10278-013-9622-7"},{"key":"ref91","doi-asserted-by":"publisher","DOI":"10.5114\/wo.2014.47136"},{"key":"ref92","doi-asserted-by":"publisher","DOI":"10.1007\/s10479-011-0841-3"},{"key":"ref93","doi-asserted-by":"publisher","DOI":"10.1145\/3359786"},{"key":"ref94","first-page":"267","article-title":"The secret sharer: Evaluating and testing unintended memorization in neural networks","volume-title":"Proc. 28th USENIX Secur. Symp.","author":"Carlini","year":"2019"},{"key":"ref95","doi-asserted-by":"publisher","DOI":"10.1126\/science.aaw4399"},{"key":"ref96","doi-asserted-by":"publisher","DOI":"10.1186\/s12916-019-1426-2"},{"key":"ref97","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2016.05.004"},{"key":"ref98","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2019.02.036"},{"key":"ref99","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-63285-0"},{"key":"ref100","doi-asserted-by":"publisher","DOI":"10.3390\/cancers12030603"},{"key":"ref101","doi-asserted-by":"publisher","DOI":"10.1002\/acm2.12554"},{"key":"ref102","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ab843e"},{"key":"ref103","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2017.2715284"},{"key":"ref104","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2827462"},{"key":"ref105","first-page":"1659","article-title":"Interpretable deep learning under fire","volume-title":"Proc. 29th USENIX Secur. Symp.","author":"Zhang","year":"2018"},{"key":"ref106","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-27272-2_20"},{"key":"ref107","doi-asserted-by":"publisher","DOI":"10.1109\/EUROCON.2019.8861636"},{"key":"ref108","doi-asserted-by":"publisher","DOI":"10.1007\/s40305-019-00287-4"},{"key":"ref109","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-28954-6_14"},{"key":"ref110","first-page":"9505","article-title":"Sanity Checks for Saliency Maps","volume-title":"Advances in Neural Information Processing Systems 31","author":"Adebayo","year":"2018"},{"key":"ref111","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-018-0107-6"},{"key":"ref112","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2016.2636665"}],"container-title":["IEEE Reviews in Biomedical Engineering"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/4664312\/10007429\/09629296.pdf?arnumber=9629296","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T19:39:00Z","timestamp":1709321940000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9629296\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"references-count":112,"URL":"https:\/\/doi.org\/10.1109\/rbme.2021.3131358","relation":{},"ISSN":["1937-3333","1941-1189"],"issn-type":[{"value":"1937-3333","type":"print"},{"value":"1941-1189","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]}}}