{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T11:09:25Z","timestamp":1779275365578,"version":"3.51.4"},"reference-count":78,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,2,15]],"date-time":"2023-02-15T00:00:00Z","timestamp":1676419200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,2,15]],"date-time":"2023-02-15T00:00:00Z","timestamp":1676419200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Healthc Inform Res"],"published-print":{"date-parts":[[2023,3]]},"DOI":"10.1007\/s41666-023-00127-4","type":"journal-article","created":{"date-parts":[[2023,2,15]],"date-time":"2023-02-15T16:56:36Z","timestamp":1676480196000},"page":"59-83","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Interpretable Skin Cancer Classification based on Incremental Domain Knowledge Learning"],"prefix":"10.1007","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2531-0799","authenticated-orcid":false,"given":"Eman","family":"Rezk","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6978-0015","authenticated-orcid":false,"given":"Mohamed","family":"Eltorki","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8617-261X","authenticated-orcid":false,"given":"Wael","family":"El-Dakhakhni","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,2,15]]},"reference":[{"key":"127_CR1","doi-asserted-by":"publisher","unstructured":"Siegel RL, Miller KD, Fuchs HE, Jemal A (2022) Cancer statistics. CA Cancer J Clin 72:7\u201333. https:\/\/doi.org\/10.3322\/caac.21708","DOI":"10.3322\/caac.21708"},{"key":"127_CR2","unstructured":"American Cancer Society (2022) Cancer facts & figures\u00a02022.\u00a0https:\/\/www.cancer.org\/research\/cancer-facts-statistics\/all-cancer-facts-figures\/cancer-facts-figures-2022.html. Accessed 12 June\u00a02022"},{"key":"127_CR3","unstructured":"American Academy of Dermatology Association (AAD) (2022) Skin cancer.\u00a0https:\/\/www.aad.org\/media\/stats-skin-cancer. Accessed 12 June\u00a02022"},{"key":"127_CR4","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1016\/j.jaad.2003.07.001","volume":"50","author":"J Resneck","year":"2004","unstructured":"Resneck J, Kimball AB (2004) The dermatology workforce shortage. J Am Acad Dermatol 50:50\u201354. https:\/\/doi.org\/10.1016\/j.jaad.2003.07.001","journal-title":"J Am Acad Dermatol"},{"key":"127_CR5","doi-asserted-by":"publisher","first-page":"1265","DOI":"10.1001\/jamadermatol.2018.3022","volume":"154","author":"H Feng","year":"2018","unstructured":"Feng H, Berk-Krauss J, Feng PW, Stein JA (2018) Comparison of dermatologist density between urban and rural counties in the United States. JAMA Dermatol 154:1265\u20131271. https:\/\/doi.org\/10.1001\/jamadermatol.2018.3022","journal-title":"JAMA Dermatol"},{"key":"127_CR6","doi-asserted-by":"publisher","first-page":"1005","DOI":"10.1016\/S0190-9622(96)90137-1","volume":"35","author":"DL Ramsay","year":"1996","unstructured":"Ramsay DL, Weary PE (1996) Primary care in dermatology: whose role should it be? J Am Acad Dermatol 35:1005\u20131008. https:\/\/doi.org\/10.1016\/S0190-9622(96)90137-1","journal-title":"J Am Acad Dermatol"},{"key":"127_CR7","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1111\/j.1440-0960.2007.00340.x","volume":"48","author":"G Moreno","year":"2007","unstructured":"Moreno G, Tran H, Chia ALK, Lim A, Shumack S (2007) Prospective study to assess general practitioners\u2019 dermatological diagnostic skills in a referral setting. Australas J Dermatol 48:77\u201382. https:\/\/doi.org\/10.1111\/j.1440-0960.2007.00340.x","journal-title":"Australas J Dermatol"},{"key":"127_CR8","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1016\/j.ejca.2019.02.005","volume":"111","author":"TJ Brinker","year":"2019","unstructured":"Brinker TJ, Hekler A, Enk AH, Klode J, Hauschild A et al (2019) A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task. Eur J Cancer 111:148\u2013154. https:\/\/doi.org\/10.1016\/j.ejca.2019.02.005","journal-title":"Eur J Cancer"},{"key":"127_CR9","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.ejca.2019.05.023","volume":"119","author":"TJ Brinker","year":"2019","unstructured":"Brinker TJ, Hekler A, Enk AH, Berking C, Haferkamp S et al (2019) Deep neural networks are superior to dermatologists in melanoma image classification. Eur J Cancer 119:11\u201317. https:\/\/doi.org\/10.1016\/j.ejca.2019.05.023","journal-title":"Eur J Cancer"},{"key":"127_CR10","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.ejca.2019.04.001","volume":"113","author":"TJ Brinker","year":"2019","unstructured":"Brinker TJ, Hekler A, Enk AH, Klode J, Hauschild A et al (2019) Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. Eur J Cancer 113:47\u201354. https:\/\/doi.org\/10.1016\/j.ejca.2019.04.001","journal-title":"Eur J Cancer"},{"key":"127_CR11","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.ejca.2019.06.013","volume":"119","author":"RC Maron","year":"2019","unstructured":"Maron RC, Weichenthal M, Utikal JS, Hekler A, Berking C et al (2019) Systematic outperformance of 112 dermatologists in multiclass skin cancer image classification by convolutional neural networks. Eur J Cancer 119:57\u201365. https:\/\/doi.org\/10.1016\/j.ejca.2019.06.013","journal-title":"Eur J Cancer"},{"key":"127_CR12","doi-asserted-by":"publisher","first-page":"104065","DOI":"10.1016\/j.compbiomed.2020.104065","volume":"127","author":"M Goyal","year":"2020","unstructured":"Goyal M, Knackstedt T, Yan S, Hassanpour S (2020) Artificial intelligence-based image classification methods for diagnosis of skin cancer: challenges and opportunities. Comput Biol Med 127:104065. https:\/\/doi.org\/10.1016\/j.compbiomed.2020.104065","journal-title":"Comput Biol Med"},{"key":"127_CR13","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1016\/j.ejca.2022.02.025","volume":"167","author":"K Hauser","year":"2022","unstructured":"Hauser K, Kurz A, Haggenm\u00fcller S, Maron RC, von Kalle C et al (2022) Explainable artificial intelligence in skin cancer recognition: a systematic review. Eur J Cancer 167:54\u201369. https:\/\/doi.org\/10.1016\/j.ejca.2022.02.025","journal-title":"Eur J Cancer"},{"key":"127_CR14","doi-asserted-by":"publisher","unstructured":"Holzinger A (2021) The next frontier: AI we can really trust. Mach Learn Princ Pract Knowl Discov Databases ECML PKDD 2021, CCIS, vol 1524. Springer, Cham, pp 427\u2013440. https:\/\/doi.org\/10.1007\/978-3-030-93736-2_33","DOI":"10.1007\/978-3-030-93736-2_33"},{"key":"127_CR15","unstructured":"Madiega T, Chahri S (2022) BRIEFING: EU legislation in progress, proposal for artificial intelligence act.\u00a0https:\/\/www.europarl.europa.eu\/thinktank\/en\/document\/EPRS_BRI(2021)698792. Accessed 12 June\u00a02022"},{"key":"127_CR16","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1016\/j.inffus.2019.12.012","volume":"58","author":"A BarredoArrieta","year":"2020","unstructured":"BarredoArrieta A, D\u00edaz-Rodr\u00edguez N, Del Ser J, Bennetot A, Tabik S et al (2020) Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Inf Fusion 58:82\u2013115. https:\/\/doi.org\/10.1016\/j.inffus.2019.12.012","journal-title":"Inf Fusion"},{"key":"127_CR17","doi-asserted-by":"publisher","unstructured":"Holzinger A, Saranti A, Molnar C, Biecek P, Samek W (2022) Explainable AI methods - a brief overview. xxAI - beyond explain AI xxAI 2020 Lect Notes Comput Sci, vol 13200. Springer, Cham, pp 13\u201338. https:\/\/doi.org\/10.1007\/978-3-031-04083-2_2","DOI":"10.1007\/978-3-031-04083-2_2"},{"key":"127_CR18","doi-asserted-by":"publisher","first-page":"105111","DOI":"10.1016\/j.compbiomed.2021.105111","volume":"140","author":"Z Salahuddin","year":"2022","unstructured":"Salahuddin Z, Woodruff HC, Chatterjee A, Lambin P (2022) Transparency of deep neural networks for medical image analysis: a review of interpretability methods. Comput Biol Med 140:105111. https:\/\/doi.org\/10.1016\/j.compbiomed.2021.105111","journal-title":"Comput Biol Med"},{"key":"127_CR19","doi-asserted-by":"publisher","first-page":"e23010018","DOI":"10.3390\/e23010018","volume":"23","author":"P Linardatos","year":"2021","unstructured":"Linardatos P, Papastefanopoulos V, Kotsiantis S (2021) Explainable AI\u202f: a review of machine learning interpretability methods. MDPI Entropy 23:e23010018. https:\/\/doi.org\/10.3390\/e23010018","journal-title":"MDPI Entropy"},{"key":"127_CR20","doi-asserted-by":"publisher","first-page":"59800","DOI":"10.1109\/ACCESS.2021.3070212.A","volume":"9","author":"G Joshi","year":"2021","unstructured":"Joshi G, Walambe R, Kotecha K (2021) A review on explainability in multimodal deep neural nets. IEEE Access 9:59800\u201359821. https:\/\/doi.org\/10.1109\/ACCESS.2021.3070212.A","journal-title":"IEEE Access"},{"key":"127_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1002\/mp.15359","volume":"49","author":"JD Fuhrman","year":"2022","unstructured":"Fuhrman JD, Gorre N, Giger ML, Hu Q, Li H (2022) A review of explainable and interpretable AI with applications in COVID-19 imaging. Med Phys 49:1\u201314. https:\/\/doi.org\/10.1002\/mp.15359","journal-title":"Med Phys"},{"key":"127_CR22","doi-asserted-by":"publisher","unstructured":"Barata C, Marques JS (2019) Deep learning for skin cancer diagnosis with hierarchical architectures. IEEE 16th Int Symp Biomed Imaging 2019:841\u2013845.\u00a0https:\/\/doi.org\/10.1109\/ISBI.2019.8759561","DOI":"10.1109\/ISBI.2019.8759561"},{"key":"127_CR23","doi-asserted-by":"publisher","first-page":"2482","DOI":"10.1109\/TMI.2020.2972964","volume":"39","author":"Y Xie","year":"2020","unstructured":"Xie Y, Zhang J, Xia Y, Shen C (2020) A mutual bootstrapping model for automated skin lesion segmentation and classification. IEEE Trans Med Imaging 39:2482\u20132493. https:\/\/doi.org\/10.1109\/TMI.2020.2972964","journal-title":"IEEE Trans Med Imaging"},{"key":"127_CR24","doi-asserted-by":"publisher","first-page":"551","DOI":"10.1016\/S0190-9622(94)70061-3","volume":"30","author":"F Nachbar","year":"1994","unstructured":"Nachbar F, Stolz W, Merkle T, Cognetta AB, Vogt T, Landthaler M, Bilek P, Braun-Falco O, Plewig G (1994) The ABCD rule of dermatoscopy: High prospective value in the diagnosis of doubtful melanocytic skin lesions. J Am Acad Dermatol 30:551\u2013559. https:\/\/doi.org\/10.1016\/S0190-9622(94)70061-3","journal-title":"J Am Acad Dermatol"},{"key":"127_CR25","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1515\/aot-2021-0023","volume":"10","author":"V Blahnik","year":"2021","unstructured":"Blahnik V, Schindelbeck O (2021) Smartphone imaging technology and its applications. Adv Opt Technol 10:145\u2013232. https:\/\/doi.org\/10.1515\/aot-2021-0023","journal-title":"Adv Opt Technol"},{"issue":"2","key":"127_CR26","doi-asserted-by":"publisher","first-page":"98","DOI":"10.5826\/dpc.0902a04","volume":"9","author":"J Fee","year":"2019","unstructured":"Fee J, McGrady F, Rosendahl C, Hart N (2019) Dermoscopy use in primary care: a scoping review. Dermatol Pract Concept 9(2):98\u2013104. https:\/\/doi.org\/10.5826\/dpc.0902a04","journal-title":"Dermatol Pract Concept"},{"key":"127_CR27","doi-asserted-by":"publisher","unstructured":"Barata C, Santiago C (2021) Improving the explainability of skin cancer diagnosis using CBIR. Med Image Comput Comput Assist Interv \u2013 MICCAI 2021 Lect Notes Comput Sci, vol 12903. Springer, Cham, pp 550\u2013559. https:\/\/doi.org\/10.1007\/978-3-030-87199-4_52","DOI":"10.1007\/978-3-030-87199-4_52"},{"key":"127_CR28","doi-asserted-by":"publisher","unstructured":"Codella NCF, Lin CC, Halpern A, Hind M, Feris R et al (2018) Collaborative human-AI (CHAI): evidence-based interpretable melanoma classification in dermoscopic images. MLCN DLF IMIMIC 2018 Lect Notes Comput Sci, vol\u00a011038. Springer, Cham, pp 97\u2013105.\u00a0https:\/\/doi.org\/10.1007\/978-3-030-02628-8_11","DOI":"10.1007\/978-3-030-02628-8_11"},{"key":"127_CR29","doi-asserted-by":"publisher","first-page":"2771","DOI":"10.1001\/jama.292.22.2771","volume":"292","author":"NR Abbasi","year":"2004","unstructured":"Abbasi NR, Shaw HM, Rigel DS, Friedman RJ, Mccarthy WH et al (2004) Early diagnosis of cutaneous melanoma: revisiting the ABCD criteria. JAMA - J Am Med Assoc 292:2771\u20132776","journal-title":"JAMA - J Am Med Assoc"},{"key":"127_CR30","doi-asserted-by":"publisher","unstructured":"Chowdhury T, Bajwa ARS, Chakraborti T, Rittscher J, Pal U (2021) Exploring the correlation between deep learned and clinical features. Med Image Underst Anal MIUA\u00a02021 Lect Notes Comput Sci, vol 12722. Springer, Cham, pp 3\u201317.\u00a0https:\/\/doi.org\/10.1007\/978-3-030-80432-9_1","DOI":"10.1007\/978-3-030-80432-9_1"},{"key":"127_CR31","doi-asserted-by":"publisher","unstructured":"Stieler F, Rabe F, Bauer B (2021) Towards domain-specific explainable AI: model interpretation of a skin image classifier using a human approach. IEEE Comput Soc Conf Comput Vis Pattern Recognit Work 2021. pp\u00a01802\u20131809.\u00a0https:\/\/doi.org\/10.1109\/CVPRW53098.2021.00199","DOI":"10.1109\/CVPRW53098.2021.00199"},{"key":"127_CR32","doi-asserted-by":"publisher","unstructured":"Ribeiro MT, Singh S, Guestrin C (2016) Why should I trust you? Explaining the predictions of any classifier. The 2016 conference of the North American chapter of the association for computational linguistics: demonstrations 2016.\u00a0pp 97\u2013101.\u00a0https:\/\/doi.org\/10.18653\/v1\/n16-3020","DOI":"10.18653\/v1\/n16-3020"},{"key":"127_CR33","unstructured":"B. Kim, M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, R. Sayres, (2018) Interpretability beyond feature attribution: quantitative testing with concept activation vectors (TCAV), in: 35th Int Conf Mach Learn ICML 2018. 6:4186\u20134195"},{"key":"127_CR34","doi-asserted-by":"publisher","first-page":"106620","DOI":"10.1016\/j.cmpb.2022.106620","volume":"215","author":"A Lucieri","year":"2022","unstructured":"Lucieri A, Bajwa MN, Braun SA, Malik MI, Dengel A, Ahmed S (2022) ExAID: A multimodal explanation framework for computer-aided diagnosis of skin lesions. Comput Methods Programs Biomed 215:106620. https:\/\/doi.org\/10.1016\/j.cmpb.2022.106620","journal-title":"Comput Methods Programs Biomed"},{"key":"127_CR35","doi-asserted-by":"publisher","first-page":"43","DOI":"10.5826\/dpc.0203a08","volume":"2","author":"P Tschandl","year":"2012","unstructured":"Tschandl P, Rosendahl C, Kittler H (2012) Accuracy of the first step of the dermatoscopic 2-step algorithm for pigmented skin lesions. Dermatol Pract Concept 2:43\u201349. https:\/\/doi.org\/10.5826\/dpc.0203a08","journal-title":"Dermatol Pract Concept"},{"key":"127_CR36","doi-asserted-by":"publisher","first-page":"107413","DOI":"10.1016\/j.patcog.2020.107413","volume":"110","author":"C Barata","year":"2021","unstructured":"Barata C, Celebi ME, Marques JS (2021) Explainable skin lesion diagnosis using taxonomies. Pattern Recognit 110:107413. https:\/\/doi.org\/10.1016\/j.patcog.2020.107413","journal-title":"Pattern Recognit"},{"key":"127_CR37","doi-asserted-by":"publisher","unstructured":"Zhou B, Khosla A, Lapedriza A, Oliva A, Torralba A (2016) Learning deep features for discriminative localization. IEEE Comput Soc Conf Comput Vis Pattern Recognit\u00a02016. pp 2921\u20132929. https:\/\/doi.org\/10.1109\/CVPR.2016.319","DOI":"10.1109\/CVPR.2016.319"},{"key":"127_CR38","doi-asserted-by":"publisher","unstructured":"Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D et al (2017) Grad-CAM: visual explanations from deep networks via gradient-based localization. IEEE Int Conf Comput Vis 2017.\u00a0pp 618\u2013626. https:\/\/doi.org\/10.1109\/ICCV.2017.74","DOI":"10.1109\/ICCV.2017.74"},{"key":"127_CR39","doi-asserted-by":"publisher","first-page":"65130","DOI":"10.1109\/ACCESS.2018.2877587","volume":"6","author":"J Yang","year":"2018","unstructured":"Yang J, Xie F, Fan H, Jiang Z, Liu J (2018) Classification for dermoscopy images using convolutional neural networks based on region average pooling. IEEE Access 6:65130\u201365138. https:\/\/doi.org\/10.1109\/ACCESS.2018.2877587","journal-title":"IEEE Access"},{"key":"127_CR40","doi-asserted-by":"publisher","first-page":"99633","DOI":"10.1109\/ACCESS.2020.2997710","volume":"8","author":"L Wei","year":"2020","unstructured":"Wei L, Ding K, Hu H (2020) Automatic skin cancer detection in dermoscopy images based on ensemble lightweight deep learning network. IEEE Access 8:99633\u201399647. https:\/\/doi.org\/10.1109\/ACCESS.2020.2997710","journal-title":"IEEE Access"},{"key":"127_CR41","doi-asserted-by":"publisher","unstructured":"Zunair H, Ben Hamza A (2020) Melanoma detection using adversarial training and deep transfer learning. Phys Med Biol 65:135005 https:\/\/doi.org\/10.1088\/1361-6560\/ab86d3","DOI":"10.1088\/1361-6560\/ab86d3"},{"key":"127_CR42","doi-asserted-by":"crossref","unstructured":"Li W, Zhuang J, Wang R, Zhang J (2020) Fusing metadata and dermoscopy images for skin disease diagnosis. IEEE 17th Int Symp Biomed Imaging 2020.\u00a0pp\u00a01996\u20132000","DOI":"10.1109\/ISBI45749.2020.9098645"},{"key":"127_CR43","doi-asserted-by":"publisher","unstructured":"Nunnari F, Kadir MA, Sonntag D (2021) On the overlap between Grad-CAM saliency maps and explainable visual features in skin cancer images. Mach Learn Knowl Extr, vol 12844. Springer, Cham, pp 241\u2013253. https:\/\/doi.org\/10.1007\/978-3-030-84060-0_16","DOI":"10.1007\/978-3-030-84060-0_16"},{"key":"127_CR44","doi-asserted-by":"publisher","unstructured":"Ge Z, Demyanov S, Chakravorty R, Bowling A, Garnavi R (2017) Skin disease recognition using deep saliency features and multimodal learning of dermoscopy and clinical images. Med Image Comput Comput Assist Interv \u2212 MICCAI 2017 Lect Notes Comput Sci, vol 10435. pp 250\u2013258. https:\/\/doi.org\/10.1007\/978-3-319-66179-7_29","DOI":"10.1007\/978-3-319-66179-7_29"},{"key":"127_CR45","doi-asserted-by":"publisher","unstructured":"Lin TY, Roychowdhury A, Maji S (2015) Bilinear CNN models for fine-grained visual recognition. IEEE Int Conf Comput Vis 2015. pp 1449\u20131457.\u00a0https:\/\/doi.org\/10.1109\/ICCV.2015.170","DOI":"10.1109\/ICCV.2015.170"},{"key":"127_CR46","doi-asserted-by":"publisher","first-page":"1529","DOI":"10.1016\/j.jid.2018.01.028","volume":"138","author":"SS Han","year":"2018","unstructured":"Han SS, Kim MS, Lim W, Park GH, Park I et al (2018) Classification of the clinical images for benign and malignant cutaneous tumors using a deep learning algorithm. J Invest Dermatol 138:1529\u20131538. https:\/\/doi.org\/10.1016\/j.jid.2018.01.028","journal-title":"J Invest Dermatol"},{"key":"127_CR47","doi-asserted-by":"publisher","unstructured":"Pfau J, Young AT, Wei ML, Keiser MJ (2019) Global saliency: aggregating saliency maps to assess dataset artefact bias. Machine Learning for Health (ML4H) Workshop at NeurIPS 2019. pp 1\u20139. \nhttps:\/\/doi.org\/10.48550\/arXiv.1910.07604","DOI":"10.48550\/arXiv.1910.07604"},{"key":"127_CR48","doi-asserted-by":"publisher","unstructured":"Gupta A, Arora S (2019) A simple saliency method that passes the sanity checks. ArXiv 2019.\u00a0pp 1\u201311. \nhttps:\/\/doi.org\/10.48550\/arXiv.1905.12152","DOI":"10.48550\/arXiv.1905.12152"},{"key":"127_CR49","doi-asserted-by":"publisher","first-page":"538","DOI":"10.1109\/JBHI.2018.2824327","volume":"23","author":"J Kawahara","year":"2019","unstructured":"Kawahara J, Daneshvar S, Argenziano G, Hamarneh G (2019) Seven-point checklist and skin lesion classification using multitask multimodal neural nets. IEEE J Biomed Heal Informatics 23:538\u2013546. https:\/\/doi.org\/10.1109\/JBHI.2018.2824327","journal-title":"IEEE J Biomed Heal Informatics"},{"key":"127_CR50","doi-asserted-by":"publisher","first-page":"1563","DOI":"10.1001\/archderm.134.12.1563","volume":"134","author":"G Argenziano","year":"1998","unstructured":"Argenziano G, Fabbrocini G, Carli P, De Giorgi V, Sammarco E et al (1998) Epiluminescence microscopy for the diagnosis of doubtful melanocytic skin lesions: comparison of the ABCD rule of dermatoscopy and a new 7-point checklist based on pattern analysis. Arch Dermatol 134:1563\u20131570. https:\/\/doi.org\/10.1001\/archderm.134.12.1563","journal-title":"Arch Dermatol"},{"key":"127_CR51","unstructured":"Sundararajan M, Taly A, Yan Q (2017) Axiomatic attribution for deep networks. The 34th Int Conf Mach Learn, vol 70. pp 3319\u20133328"},{"key":"127_CR52","doi-asserted-by":"publisher","first-page":"900","DOI":"10.1038\/s41591-020-0842-3","volume":"26","author":"Y Liu","year":"2020","unstructured":"Liu Y, Jain A, Eng C, Way DH, Lee K et al (2020) A deep learning system for differential diagnosis of skin diseases. Nat Med 26:900\u2013908. https:\/\/doi.org\/10.1038\/s41591-020-0842-3","journal-title":"Nat Med"},{"key":"127_CR53","doi-asserted-by":"publisher","unstructured":"Smilkov D, Thorat N, Kim B, Vi\u00e9gas F, Wattenberg M (2017) SmoothGrad: removing noise by adding noise. ArXiv 2017.\u00a0pp 1\u201310. https:\/\/doi.org\/10.48550\/arXiv.1706.03825","DOI":"10.48550\/arXiv.1706.03825"},{"key":"127_CR54","doi-asserted-by":"publisher","unstructured":"Singh N, Lee K, Coz D, Angermueller C, Huang S et al (2020) Agreement between saliency maps and human-labeled regions of interest: applications to skin disease classification. IEEE Comput Soc Conf Comput Vis Pattern Recognit Work 2020. pp 3172\u20133181.\u00a0https:\/\/doi.org\/10.1109\/CVPRW50498.2020.00376","DOI":"10.1109\/CVPRW50498.2020.00376"},{"key":"127_CR55","unstructured":"Seven point Criteria Evaluation Database (2019). https:\/\/derm.cs.sfu.ca\/Welcome.html. Accessed 20 May\u00a02022"},{"key":"127_CR56","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1016\/j.patrec.2020.03.004","volume":"133","author":"R Zhu","year":"2020","unstructured":"Zhu R, Guo Y, Xue JH (2020) Adjusting the imbalance ratio by the dimensionality of imbalanced data. Pattern Recognit Lett 133:217\u2013223. https:\/\/doi.org\/10.1016\/j.patrec.2020.03.004","journal-title":"Pattern Recognit Lett"},{"key":"127_CR57","doi-asserted-by":"publisher","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. IEEE Comput Soc Conf Comput Vis Pattern Recognit\u00a02016.\u00a0pp 2818\u20132826. https:\/\/doi.org\/10.1109\/CVPR.2016.308","DOI":"10.1109\/CVPR.2016.308"},{"key":"127_CR58","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1038\/nature21056","volume":"542","author":"A Esteva","year":"2017","unstructured":"Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM et al (2017) Dermatologist-level classification of skin cancer with deep neural networks. Nature 542:115\u2013118. https:\/\/doi.org\/10.1038\/nature21056","journal-title":"Nature"},{"key":"127_CR59","unstructured":"Gordon-Rodriguez E, Loaiza-Ganem G, Pleiss G, Cunningham JP (2020) Uses and abuses of the cross-entropy loss: case studies in modern deep learning. Mach Learn Res ICBINB, NeurIPS, PMLR 37:1\u201310. https:\/\/proceedings.mlr.press\/v137\/gordon-rodriguez20a.html. Accessed 12 June\u00a02022"},{"key":"127_CR60","unstructured":"Molnar C (2022) Neural networks interpretation. Interpretable Machine Learning: a Guide for Making Black Box Model Explainable Second edition chapter 10:444\u2013473"},{"key":"127_CR61","doi-asserted-by":"publisher","unstructured":"Chattopadhay A, Sarkar A, Howlader P, Balasubramanian VN (2018) Grad-CAM++: generalized gradient-based visual explanations for deep convolutional networks. IEEE Winter Conf Appl Comput Vision, WACV 2018.\u00a0pp 839\u2013847.\u00a0https:\/\/doi.org\/10.1109\/WACV.2018.00097","DOI":"10.1109\/WACV.2018.00097"},{"key":"127_CR62","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1186\/s40537-019-0192-5","volume":"6","author":"JM Johnson","year":"2019","unstructured":"Johnson JM, Khoshgoftaar TM (2019) Survey on deep learning with class imbalance. J Big Data 6:6\u201327. https:\/\/doi.org\/10.1186\/s40537-019-0192-5","journal-title":"J Big Data"},{"key":"127_CR63","doi-asserted-by":"publisher","unstructured":"Sugino T, Kawase T, Onogi S, Kin T, Saito N et al (2021) Loss weightings for improving imbalanced brain structure segmentation using fully convolutional networks. MDPI Healthc 9(8):938.\u00a0https:\/\/doi.org\/10.3390\/healthcare9080938","DOI":"10.3390\/healthcare9080938"},{"key":"127_CR64","doi-asserted-by":"publisher","unstructured":"Cui Y, Jia M, Lin TY, Song Y, Belongie S (2019) Class-balanced loss based on effective number of samples. IEEE Comput Soc Conf Comput Vis Pattern Recognit 2019. pp 9260\u20139269. https:\/\/doi.org\/10.1109\/CVPR.2019.00949","DOI":"10.1109\/CVPR.2019.00949"},{"key":"127_CR65","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1186\/s40537-019-0197-0","volume":"6","author":"C Shorten","year":"2019","unstructured":"Shorten C, Khoshgoftaar TM (2019) A survey on image data augmentation for deep learning. J Big Data 6:60. https:\/\/doi.org\/10.1186\/s40537-019-0197-0","journal-title":"J Big Data"},{"key":"127_CR66","unstructured":"DermNet NZ, (2013). https:\/\/dermnetnz.org\/ Accessed 7 Feb 2022"},{"key":"127_CR67","unstructured":"Kumar R (2019) Cross-validation and model selection. Machine learning quick reference: quick and essential machine learning hacks for training smart data models. Packet Publishing,\u00a0pp\u00a027\u201329."},{"key":"127_CR68","unstructured":"Cuemath Z Test, (2016). https:\/\/www.cuemath.com\/data\/z-test\/ Accessed 24 Nov 2022"},{"key":"127_CR69","doi-asserted-by":"publisher","unstructured":"Tan C, Sun F, Kong T, Zhang W, Yang C et al (2018) A survey on deep transfer learning. Artificial Neural Networks and Machine Learning \u2013 ICANN 2018. ICANN 2018, Lecture Notes in Computer Science, vol 11141. pp 70\u2013279.\u00a0https:\/\/doi.org\/10.1007\/978-3-030-01424-7_27","DOI":"10.1007\/978-3-030-01424-7_27"},{"key":"127_CR70","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky O, Deng J, Su H, Krause J, Satheesh S et al (2015) ImageNet large scale visual recognition challenge. Int J Comput Vis 115:211\u2013252. https:\/\/doi.org\/10.1007\/s11263-015-0816-y","journal-title":"Int J Comput Vis"},{"key":"127_CR71","doi-asserted-by":"publisher","unstructured":"Lin M, Chen Q, Yan S (2014) Network in network. The 2nd Int Conf on Learn Rep ICLR 2014. pp 1\u201310. https:\/\/doi.org\/10.48550\/arXiv.1312.4400","DOI":"10.48550\/arXiv.1312.4400"},{"key":"127_CR72","doi-asserted-by":"publisher","unstructured":"Ruder S (2017) An overview of gradient descent optimization algorithms. ArXiv 2017. pp 1\u201314.\u00a0https:\/\/doi.org\/10.48550\/arXiv.1609.04747","DOI":"10.48550\/arXiv.1609.04747"},{"key":"127_CR73","unstructured":"Chollet F (2015) Keras. https:\/\/github.com\/fchollet\/keras. Accessed 24 Apr\u00a02022"},{"key":"127_CR74","unstructured":"Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z et al (2015) TensorFlow: large-scale machine learning on heterogeneous distributed systems. https:\/\/tensorflow.org. Accessed 24 Apr\u00a02022"},{"key":"127_CR75","doi-asserted-by":"publisher","first-page":"01","DOI":"10.5121\/ijdkp.2015.5201","volume":"5","author":"M Hossin","year":"2015","unstructured":"Hossin M, Sulaiman M (2015) A review on evaluation metrics for data classification evaluations. Int J Data Min Knowl Manag Process 5:01\u201311. https:\/\/doi.org\/10.5121\/ijdkp.2015.5201","journal-title":"Int J Data Min Knowl Manag Process"},{"key":"127_CR76","unstructured":"Ngiam J, Chen Z, Koh PW, Ng AY (2011) Learning deep energy models. The 28th Int Conf Mach Learn ICML 2011. pp 1105\u20131112"},{"key":"127_CR77","doi-asserted-by":"publisher","unstructured":"Gao Z, Wu Y, Zhang X, Dai J, Jia Y, et al (2020) Revisiting bilinear pooling: a coding perspective. The 34th AAAI Conf Artif Intell 2020. pp 3954\u20133961.\u00a0https:\/\/doi.org\/10.1609\/aaai.v34i04.5811","DOI":"10.1609\/aaai.v34i04.5811"},{"key":"127_CR78","doi-asserted-by":"publisher","first-page":"2579","DOI":"10.1007\/s10479-011-0841-3","volume":"9","author":"L van der Maaten","year":"2008","unstructured":"van der Maaten L, Hinton G (2008) Visualizing data using t-SNE. Mach Learn Res 9:2579\u20132605. https:\/\/doi.org\/10.1007\/s10479-011-0841-3","journal-title":"Mach Learn Res"}],"container-title":["Journal of Healthcare Informatics Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41666-023-00127-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s41666-023-00127-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41666-023-00127-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,8]],"date-time":"2023-03-08T13:22:42Z","timestamp":1678281762000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s41666-023-00127-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,15]]},"references-count":78,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023,3]]}},"alternative-id":["127"],"URL":"https:\/\/doi.org\/10.1007\/s41666-023-00127-4","relation":{},"ISSN":["2509-4971","2509-498X"],"issn-type":[{"value":"2509-4971","type":"print"},{"value":"2509-498X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,15]]},"assertion":[{"value":"21 July 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 January 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 February 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 February 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}]}}