{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T13:27:41Z","timestamp":1793366861262,"version":"4.5.0"},"reference-count":69,"publisher":"Elsevier BV","issue":"4","license":[{"start":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T00:00:00Z","timestamp":1719792000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T00:00:00Z","timestamp":1719792000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,7,23]],"date-time":"2024-07-23T00:00:00Z","timestamp":1721692800000},"content-version":"vor","delay-in-days":22,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Asia-Pacific Journal of Ophthalmology"],"published-print":{"date-parts":[[2024,7]]},"DOI":"10.1016\/j.apjo.2024.100087","type":"journal-article","created":{"date-parts":[[2024,7,26]],"date-time":"2024-07-26T19:48:21Z","timestamp":1722023301000},"page":"100087","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":21,"title":["The role of saliency maps in enhancing ophthalmologists\u2019 trust in artificial intelligence models"],"prefix":"10.1016","volume":"13","author":[{"given":"Carolyn Yu Tung","family":"Wong","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6679-7276","authenticated-orcid":false,"given":"Fares","family":"Antaki","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7913-1403","authenticated-orcid":false,"given":"Peter","family":"Woodward-Court","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9300-573X","authenticated-orcid":false,"given":"Ariel Yuhan","family":"Ong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pearse A.","family":"Keane","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.apjo.2024.100087_bib1","doi-asserted-by":"crossref","DOI":"10.1155\/2018\/7068349","article-title":"Deep learning for computer vision: A brief review","volume":"2018","author":"Voulodimos","year":"2018","journal-title":"Comput Intell Neurosci"},{"issue":"7","key":"10.1016\/j.apjo.2024.100087_bib2","article-title":"Artificial intelligence in ophthalmology: The path to the real-world clinic","volume":"4","author":"Li","year":"2023","journal-title":"Cell Rep Med"},{"issue":"1","key":"10.1016\/j.apjo.2024.100087_bib3","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1186\/s12913-022-07655-6","article-title":"Cost-effectiveness of artificial intelligence screening for diabetic retinopathy in rural China","volume":"22","author":"Huang","year":"2022","journal-title":"BMC Health Serv Res"},{"issue":"8","key":"10.1016\/j.apjo.2024.100087_bib4","article-title":"Artificial intelligence in eye disease: Recent developments, applications, and surveys","volume":"12","author":"Han","year":"2022","journal-title":"Diagn (Basel)"},{"issue":"1","key":"10.1016\/j.apjo.2024.100087_bib5","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1016\/j.survophthal.2021.03.003","article-title":"Artificial intelligence: the unstoppable revolution in ophthalmology","volume":"67","author":"Benet","year":"2022","journal-title":"Surv Ophthalmol"},{"key":"10.1016\/j.apjo.2024.100087_bib6","first-page":"31","article-title":"Quantitative evaluations on saliency methods: An experimental study","author":"Li","year":"2020","journal-title":"Publ Online Dec"},{"issue":"3","key":"10.1016\/j.apjo.2024.100087_bib7","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1111\/jgh.15384","article-title":"Opening the black box of AI-Medicine","volume":"36","author":"Poon","year":"2021","journal-title":"J Gastroenterol Hepatol"},{"key":"10.1016\/j.apjo.2024.100087_bib8","doi-asserted-by":"crossref","DOI":"10.3389\/fonc.2023.1184428","article-title":"Editorial: Interpretable and explainable machine learning models in oncology","volume":"13","author":"Hrinivich","year":"2023","journal-title":"Front Oncol"},{"issue":"2","key":"10.1016\/j.apjo.2024.100087_bib9","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1016\/j.cjca.2021.09.004","article-title":"Opening the black box: The promise and limitations of explainable machine learning in cardiology","volume":"38","author":"Petch","year":"2022","journal-title":"Can J Cardiol"},{"key":"10.1016\/j.apjo.2024.100087_bib10","article-title":"Model-agnostic vs. model-intrinsic interpretability for explainable product search","author":"Ai","year":"2021","journal-title":"Publ Online August 11"},{"key":"10.1016\/j.apjo.2024.100087_bib11","unstructured":"Molnar C. 10.2 Pixel Attribution (Saliency Maps). Published August 21, 2023. \u3008https:\/\/christophm.github.io\/interpretable-ML-book\/pixel-attribution.html\u3009. Accessed November 25, 2023."},{"key":"10.1016\/j.apjo.2024.100087_bib12","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2022.102364","article-title":"Clinical validation of saliency maps for understanding deep neural networks in ophthalmology","volume":"77","author":"Ayhan","year":"2022","journal-title":"Med Image Anal"},{"issue":"2","key":"10.1016\/j.apjo.2024.100087_bib13","doi-asserted-by":"crossref","DOI":"10.1016\/j.ipm.2022.103225","article-title":"Evaluating the faithfulness of saliency maps in explaining deep learning models using realistic perturbations","volume":"60","author":"Amorim","year":"2023","journal-title":"Inf Process Manag"},{"key":"10.1016\/j.apjo.2024.100087_bib14","first-page":"1959","article-title":"DBCE: a saliency method for medical deep learning through anatomically-consistent free-form deformations. In: Proceedings of","author":"Peters","year":"2023","journal-title":"IEEE\/CVF Winter Conf Appl Comput Vis"},{"key":"10.1016\/j.apjo.2024.100087_bib15","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1186\/s40662-020-00183-6","article-title":"Application of machine learning in ophthalmic imaging modalities","volume":"7","author":"Tong","year":"2020","journal-title":"Eye Vis (Lond)"},{"issue":"5","key":"10.1016\/j.apjo.2024.100087_bib16","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0233079","article-title":"Deep learning assisted detection of glaucomatous optic neuropathy and potential designs for a generalizable model","volume":"15","author":"Ko","year":"2020","journal-title":"PLoS One"},{"issue":"1","key":"10.1016\/j.apjo.2024.100087_bib17","doi-asserted-by":"crossref","DOI":"10.1016\/j.xops.2022.100233","article-title":"Detecting glaucoma from fundus photographs using deep learning without convolutions: Transformer for improved generalization","volume":"3","author":"Fan","year":"2023","journal-title":"Ophthalmol Sci"},{"issue":"1","key":"10.1016\/j.apjo.2024.100087_bib18","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.ophtha.2020.06.036","article-title":"Explaining the rationale of deep learning glaucoma decisions with adversarial examples","volume":"128","author":"Chang","year":"2021","journal-title":"Ophthalmology"},{"issue":"4","key":"10.1016\/j.apjo.2024.100087_bib19","doi-asserted-by":"crossref","first-page":"e172","DOI":"10.1016\/S2589-7500(19)30085-8","article-title":"Detection of glaucomatous optic neuropathy with spectral-domain optical coherence tomography: a retrospective training and validation deep-learning analysis","volume":"1","author":"Ran","year":"2019","journal-title":"Lancet Digit Health"},{"issue":"5","key":"10.1016\/j.apjo.2024.100087_bib20","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1167\/tvst.11.5.11","article-title":"Deep learning for glaucoma detection and identification of novel diagnostic areas in diverse real-world datasets","volume":"11","author":"Noury","year":"2022","journal-title":"Transl Vis Sci Technol"},{"issue":"7","key":"10.1016\/j.apjo.2024.100087_bib21","article-title":"Understanding deep learning decision for glaucoma detection using 3D volumes","volume":"61","author":"George","year":"2020","journal-title":"Invest Ophthalmol Vis Sci"},{"issue":"9","key":"10.1016\/j.apjo.2024.100087_bib22","article-title":"A 3D deep learning system for detecting glaucomatous optic neuropathy from volumetric and en face optical coherence tomography scans","volume":"60","author":"Anran","year":"2019","journal-title":"Invest Ophthalmol Vis Sci"},{"issue":"11","key":"10.1016\/j.apjo.2024.100087_bib23","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0206081","article-title":"A deep learning approach to automatic detection of early glaucoma from visual fields","volume":"13","author":"Kucur","year":"2018","journal-title":"PLoS One"},{"issue":"9","key":"10.1016\/j.apjo.2024.100087_bib24","doi-asserted-by":"crossref","first-page":"5398","DOI":"10.1002\/mp.16623","article-title":"Lesion detection with fine-grained image categorization for myopic traction maculopathy (MTM) using optical coherence tomography","volume":"50","author":"Huang","year":"2023","journal-title":"Med Phys"},{"issue":"5","key":"10.1016\/j.apjo.2024.100087_bib25","doi-asserted-by":"crossref","first-page":"633","DOI":"10.1136\/bjophthalmol-2020-317825","article-title":"Development and validation of a deep learning system to screen vision-threatening conditions in high myopia using optical coherence tomography images","volume":"106","author":"Li","year":"2022","journal-title":"Br J Ophthalmol"},{"issue":"13","key":"10.1016\/j.apjo.2024.100087_bib26","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1167\/tvst.10.13.10","article-title":"Automatic screening and identifying myopic maculopathy on optical coherence tomography images using deep learning","volume":"10","author":"Ye","year":"2021","journal-title":"Transl Vis Sci Technol"},{"issue":"2","key":"10.1016\/j.apjo.2024.100087_bib27","doi-asserted-by":"crossref","first-page":"95","DOI":"10.3341\/kjo.2022.0059","article-title":"Deep learning-based prediction of axial length using ultra-widefield fundus photography","volume":"37","author":"Oh","year":"2023","journal-title":"Korean J Ophthalmol"},{"key":"10.1016\/j.apjo.2024.100087_bib28","article-title":"Development and validation of a deep learning model to predict axial length from ultra-wide field images","author":"Wang","year":"2023","journal-title":"Eye"},{"issue":"1","key":"10.1016\/j.apjo.2024.100087_bib29","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1186\/s12886-020-01657-w","article-title":"Computer-aided recognition of myopic tilted optic disc using deep learning algorithms in fundus photography","volume":"20","author":"Cho","year":"2020","journal-title":"BMC Ophthalmol"},{"issue":"11","key":"10.1016\/j.apjo.2024.100087_bib30","doi-asserted-by":"crossref","first-page":"705","DOI":"10.21037\/atm.2019.12.39","article-title":"Automatic identification of myopia based on ocular appearance images using deep learning","volume":"8","author":"Yang","year":"2020","journal-title":"Ann Transl Med"},{"issue":"4","key":"10.1016\/j.apjo.2024.100087_bib31","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1167\/tvst.12.4.12","article-title":"Multitask learning for activity detection in neovascular age-related macular degeneration","volume":"12","author":"Ayhan","year":"2023","journal-title":"Transl Vis Sci Technol"},{"issue":"8","key":"10.1016\/j.apjo.2024.100087_bib32","doi-asserted-by":"crossref","first-page":"1133","DOI":"10.1136\/bjophthalmol-2020-316984","article-title":"Detection of features associated with neovascular age-related macular degeneration in ethnically distinct data sets by an optical coherence tomography: trained deep learning algorithm","volume":"105","author":"Rim","year":"2021","journal-title":"Br J Ophthalmol"},{"issue":"9","key":"10.1016\/j.apjo.2024.100087_bib33","doi-asserted-by":"crossref","first-page":"4926","DOI":"10.1002\/mp.15002","article-title":"Attention-based deep learning system for automated diagnoses of age-related macular degeneration in optical coherence tomography images","volume":"48","author":"Yan","year":"2021","journal-title":"Med Phys"},{"key":"10.1016\/j.apjo.2024.100087_bib34","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2022.105368","article-title":"Multi-scale convolutional neural network for automated AMD classification using retinal OCT images","volume":"144","author":"Sotoudeh-Paima","year":"2022","journal-title":"Comput Biol Med"},{"issue":"4","key":"10.1016\/j.apjo.2024.100087_bib35","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1007\/s40123-019-00207-y","article-title":"Optical coherence tomography-based deep-learning models for classifying normal and age-related macular degeneration and exudative and non-exudative age-related macular degeneration changes","volume":"8","author":"Motozawa","year":"2019","journal-title":"Ophthalmol Ther"},{"issue":"7","key":"10.1016\/j.apjo.2024.100087_bib36","doi-asserted-by":"crossref","first-page":"2217","DOI":"10.1007\/s00417-022-05565-1","article-title":"Clinically applicable deep learning-based decision aids for treatment of neovascular AMD","volume":"260","author":"Gutfleisch","year":"2022","journal-title":"Graefes Arch Clin Exp Ophthalmol"},{"issue":"1","key":"10.1016\/j.apjo.2024.100087_bib37","doi-asserted-by":"crossref","DOI":"10.1136\/bmjophth-2020-000569","article-title":"Development and validation of a novel prognostic model for predicting AMD progression using longitudinal fundus images","volume":"5","author":"Bridge","year":"2020","journal-title":"BMJ Open Ophthalmol"},{"issue":"1","key":"10.1016\/j.apjo.2024.100087_bib38","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.ajo.2004.02.021","article-title":"Optic disk appearance in advanced age-related macular degeneration","volume":"138","author":"Law","year":"2004","journal-title":"Am J Ophthalmol"},{"issue":"13","key":"10.1016\/j.apjo.2024.100087_bib39","article-title":"Optic neuropathy and age\u2013related macular degeneration","volume":"45","author":"Scheufele","year":"2004","journal-title":"Invest Ophthalmol Vis Sci"},{"key":"10.1016\/j.apjo.2024.100087_bib40","doi-asserted-by":"crossref","first-page":"2751695","DOI":"10.1155\/2021\/2751695","article-title":"Data homogeneity effect in deep learning-based prediction of type 1 diabetic retinopathy","volume":"2021","author":"Lo","year":"2021","journal-title":"J Diabetes Res"},{"issue":"1","key":"10.1016\/j.apjo.2024.100087_bib41","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-021-93632-8","article-title":"An interpretable multiple-instance approach for the detection of referable diabetic retinopathy in fundus images","volume":"11","author":"Papadopoulos","year":"2021","journal-title":"Sci Rep"},{"key":"10.1016\/j.apjo.2024.100087_bib42","unstructured":"Emily Henderson B.S. Saliency heat maps may not be yet ready for prime time, study shows. News-Medical.net. Published October 10, 2022. https:\/\/www.news-medical.net\/news\/20221010\/Saliency-heat-maps-may-not-be-yet-ready-for-prime-time-study-shows.aspx. Accessed November 25, 2023."},{"issue":"5","key":"10.1016\/j.apjo.2024.100087_bib43","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1038\/s42256-019-0048-x","article-title":"Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead","volume":"1","author":"Rudin","year":"2019","journal-title":"Nat Mach Intell"},{"key":"10.1016\/j.apjo.2024.100087_bib44","doi-asserted-by":"crossref","unstructured":"Tursun O., Denman S., Sridharan S., Fookes C. SESS: Saliency Enhancing with Scaling and Sliding. arXiv [csCV]. Published online July 5, 2022. http:\/\/arxiv.org\/abs\/2207.01769.","DOI":"10.1007\/978-3-031-19775-8_19"},{"key":"10.1016\/j.apjo.2024.100087_bib45","article-title":"Puzzle-CAM: Improved localization via matching partial and full features","author":"Jo","year":"2021","journal-title":"arXiv [csCV]"},{"key":"10.1016\/j.apjo.2024.100087_bib46","article-title":"Self-supervised equivariant attention mechanism for weakly supervised semantic segmentation","author":"Wang","year":"2020","journal-title":"arXiv [csCV]"},{"issue":"5","key":"10.1016\/j.apjo.2024.100087_bib47","doi-asserted-by":"crossref","first-page":"963","DOI":"10.1080\/13658816.2023.2191256","article-title":"Explainable GeoAI: can saliency maps help interpret artificial intelligence\u2019s learning process? An empirical study on natural feature detection","volume":"37","author":"Hsu","year":"2023","journal-title":"Int J Geogr Inf Sci"},{"key":"10.1016\/j.apjo.2024.100087_bib48","unstructured":"Li J. Visual Attention is Beyond One Single Saliency Map. arXiv [csCV]. Published online October 23, 2018. \u3008http:\/\/arxiv.org\/abs\/1811.02650\u3009."},{"key":"10.1016\/j.apjo.2024.100087_bib49","unstructured":"Sanity checks for saliency maps. https:\/\/proceedings.neurips.cc\/paper\/8160-sanity-checks-for-saliency-maps. Accessed November 25, 2023."},{"issue":"6","key":"10.1016\/j.apjo.2024.100087_bib50","doi-asserted-by":"crossref","DOI":"10.1148\/ryai.2021200267","article-title":"Assessing the trustworthiness of saliency maps for localizing abnormalities in medical imaging","volume":"3","author":"Arun","year":"2021","journal-title":"Radio Artif Intell"},{"key":"10.1016\/j.apjo.2024.100087_bib51","unstructured":"Singh A., Jothi Balaji J., Rasheed M.A., Jayakumar V., Raman R., Lakshminarayanan V. Quantitative and Qualitative Evaluation of Explainable Deep Learning Methods for Ophthalmic Diagnosis. arXiv [eessIV]. Published online September 26, 2020. \u3008http:\/\/arxiv.org\/abs\/2009.12648\u3009."},{"issue":"1","key":"10.1016\/j.apjo.2024.100087_bib52","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-023-42946-w","article-title":"Ground truth based comparison of saliency maps algorithms","volume":"13","author":"Szczepankiewicz","year":"2023","journal-title":"Sci Rep"},{"key":"10.1016\/j.apjo.2024.100087_bib53","first-page":"2832","article-title":"What i cannot predict, i do not understand: A human-centered evaluation framework for explainability Methods","volume":"35","author":"Colin","year":"2022","journal-title":"Adv Neural Inf Process Syst"},{"key":"10.1016\/j.apjo.2024.100087_bib54","unstructured":"Kim S.S.Y., Meister N., Ramaswamy V.V., Fong R., Russakovsky O. HIVE: Evaluating the Human Interpretability of Visual Explanations. arXiv [csCV]. Published online December 6, 2021. \u3008http:\/\/arxiv.org\/abs\/2112.03184\u3009."},{"key":"10.1016\/j.apjo.2024.100087_bib55","unstructured":"Nguyen G., Kim D., Nguyen A. The effectiveness of feature attribution methods and its correlation with automatic evaluation scores. arXiv [csCV]. Published online May 31, 2021. \u3008http:\/\/arxiv.org\/abs\/2105.14944\u3009."},{"key":"10.1016\/j.apjo.2024.100087_bib56","unstructured":"Zhao Y., Li M., Berger M. Graphical Perception of Saliency-based Model Explanations. arXiv [csCV]. Published online June 11, 2024. \u3008http:\/\/arxiv.org\/abs\/2406.07702\u3009."},{"key":"10.1016\/j.apjo.2024.100087_bib57","unstructured":"Goodman B., Flaxman S. European Union regulations on algorithmic decision-making and a \u201cright to explanation.\u201d arXiv [statML]. Published online June 28, 2016. \u3008http:\/\/arxiv.org\/abs\/1606.08813\u3009."},{"key":"10.1016\/j.apjo.2024.100087_bib58","first-page":"76","article-title":"Comparing salient object detection results without ground truth","volume":"Vol 8691","author":"Mai","year":"2014","journal-title":"Eur Conf Comput Vis"},{"key":"10.1016\/j.apjo.2024.100087_bib59","article-title":"The road to explainability is paved with bias: Measuring the fairness of explanations","author":"Balagopalan","year":"2022","journal-title":"arXiv [csLG]"},{"key":"10.1016\/j.apjo.2024.100087_bib60","unstructured":"Yang M., Kim B. Benchmarking Attribution Methods with Relative Feature Importance. arXiv [csLG]. Published online July 23, 2019. \u3008http:\/\/arxiv.org\/abs\/1907.09701\u3009."},{"key":"10.1016\/j.apjo.2024.100087_bib61","unstructured":"Complete guide to making deep learning models generalize better. Published August 18, 2022. https:\/\/www.turing.com\/kb\/making-deep-learning-models-generalize-better. Accessed November 25, 2023."},{"key":"10.1016\/j.apjo.2024.100087_bib62","doi-asserted-by":"crossref","DOI":"10.1136\/medethics-2020-106820","article-title":"Who is afraid of black box algorithms? On the epistemological and ethical basis of trust in medical AI","author":"Dur\u00e1n","year":"2021","journal-title":"J Med Ethics"},{"issue":"2","key":"10.1016\/j.apjo.2024.100087_bib63","doi-asserted-by":"crossref","first-page":"029","DOI":"10.17352\/tcsit.000066","article-title":"Anusha. Security and privacy in federated learning: A survey","volume":"8","author":"Dasaradharami Reddy","year":"2023","journal-title":"Trends Comput Sci Inf Technol"},{"issue":"12","key":"10.1016\/j.apjo.2024.100087_bib64","doi-asserted-by":"crossref","first-page":"838","DOI":"10.1136\/jme-2022-108696","article-title":"Machine learning models, trusted research environments and UK health data: ensuring a safe and beneficial future for AI development in healthcare","volume":"49","author":"Kerasidou","year":"2023","journal-title":"J Med Ethics"},{"key":"10.1016\/j.apjo.2024.100087_bib65","article-title":"Accountability in artificial intelligence: what it is and how it works","author":"Novelli","year":"2023","journal-title":"AI Soc"},{"issue":"3","key":"10.1016\/j.apjo.2024.100087_bib66","doi-asserted-by":"crossref","first-page":"e136","DOI":"10.1016\/S2589-7500(19)30058-5","article-title":"An image-based deep learning framework for individualizing radiotherapy dose","volume":"1","author":"Lou","year":"2019","journal-title":"Lancet Digit Health"},{"issue":"4","key":"10.1016\/j.apjo.2024.100087_bib67","doi-asserted-by":"crossref","first-page":"552","DOI":"10.1016\/j.ophtha.2018.11.016","article-title":"Using a deep learning algorithm and integrated gradients explanation to assist grading for diabetic retinopathy","volume":"126","author":"Sayres","year":"2019","journal-title":"Ophthalmology"},{"issue":"7","key":"10.1016\/j.apjo.2024.100087_bib68","doi-asserted-by":"crossref","first-page":"7957","DOI":"10.1609\/aaai.v36i7.20766","article-title":"Saliency grafting: Innocuous attribution-guided mixup with calibrated label mixing","volume":"36","author":"Park","year":"2022","journal-title":"AAAI"},{"issue":"1","key":"10.1016\/j.apjo.2024.100087_bib69","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-021-89743-x","article-title":"Predicting sex from retinal fundus photographs using automated deep learning","volume":"11","author":"Korot","year":"2021","journal-title":"Sci Rep"}],"container-title":["Asia-Pacific Journal of Ophthalmology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2162098924000884?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2162098924000884?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,12,4]],"date-time":"2024-12-04T18:56:07Z","timestamp":1733338567000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2162098924000884"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7]]},"references-count":69,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,7]]}},"alternative-id":["S2162098924000884"],"URL":"https:\/\/doi.org\/10.1016\/j.apjo.2024.100087","relation":{},"ISSN":["2162-0989"],"issn-type":[{"value":"2162-0989","type":"print"}],"subject":[],"published":{"date-parts":[[2024,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"The role of saliency maps in enhancing ophthalmologists\u2019 trust in artificial intelligence models","name":"articletitle","label":"Article Title"},{"value":"Asia-Pacific Journal of Ophthalmology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.apjo.2024.100087","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 The Author(s). Published by Elsevier Inc. on behalf of Asia-Pacific Academy of Ophthalmology and Academy of Asia-Pacific Professors of Ophthalmology.","name":"copyright","label":"Copyright"}],"article-number":"100087"}}