{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T06:48:50Z","timestamp":1743058130358,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":17,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819983902"},{"type":"electronic","value":"9789819983919"}],"license":[{"start":{"date-parts":[[2023,11,27]],"date-time":"2023-11-27T00:00:00Z","timestamp":1701043200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,27]],"date-time":"2023-11-27T00:00:00Z","timestamp":1701043200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-981-99-8391-9_16","type":"book-chapter","created":{"date-parts":[[2023,11,26]],"date-time":"2023-11-26T16:02:21Z","timestamp":1701014541000},"page":"197-208","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Hybrid CNN-Interpreter: Interprete Local and\u00a0Global Contexts for\u00a0CNN-Based Models"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4885-2531","authenticated-orcid":false,"given":"Wenli","family":"Yang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9421-7344","authenticated-orcid":false,"given":"Guan","family":"Huang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2374-5067","authenticated-orcid":false,"given":"Renjie","family":"Li","sequence":"additional","affiliation":[]},{"given":"Jiahao","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Yanyu","family":"Chen","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1214-6317","authenticated-orcid":false,"given":"Quan","family":"Bai","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,11,27]]},"reference":[{"unstructured":"Liu, H., et al.: Trustworthy AI: a computational perspective. arXiv preprint arXiv:2107.06641 (2021)","key":"16_CR1"},{"issue":"2","key":"16_CR2","doi-asserted-by":"publisher","first-page":"237","DOI":"10.3390\/diagnostics12020237","volume":"12","author":"Y Zhang","year":"2022","unstructured":"Zhang, Y., Weng, Y., Lund, J.: Applications of explainable artificial intelligence in diagnosis and surgery. Diagnostics 12(2), 237 (2022)","journal-title":"Diagnostics"},{"doi-asserted-by":"crossref","unstructured":"Pawar, U., O\u2019Shea, D., Rea, S., O\u2019Reilly, R.: Explainable AI in healthcare. In: 2020 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA), pp. 1\u20132 (2020)","key":"16_CR3","DOI":"10.1109\/CyberSA49311.2020.9139655"},{"unstructured":"Atakishiyev, S., Salameh, M., Yao, H., Goebel, R.: Explainable artificial intelligence for autonomous driving: a comprehensive overview and field guide for future research directions. arXiv preprint arXiv:2112.11561 (2021)","key":"16_CR4"},{"doi-asserted-by":"crossref","unstructured":"Zhang, Q.-S., Zhu, S.-C.: Visual interpretability for deep learning: a survey. Front. Inf. Technol. Electron. Eng. 19(1), 27\u201339 (2018)","key":"16_CR5","DOI":"10.1631\/FITEE.1700808"},{"unstructured":"Bi\u0144kowski, M., Sutherland, D.J., Arbel, M., Gretton, A.: Demystifying mmd GANs. arXiv preprint arXiv:1801.01401 (2018)","key":"16_CR6"},{"key":"16_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115035","volume":"178","author":"K Waghen","year":"2021","unstructured":"Waghen, K., Ouali, M.-S.: Multi-level interpretable logic tree analysis: a data-driven approach for hierarchical causality analysis. Expert Syst. Appl. 178, 115035 (2021)","journal-title":"Expert Syst. Appl."},{"issue":"6","key":"16_CR8","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Commun. ACM 60(6), 84\u201390 (2017)","journal-title":"Commun. ACM"},{"unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)","key":"16_CR9"},{"unstructured":"Zheng, Q., Chen, Z., Liu, H., Lu, Y., Li, J.: Msranet: learning discriminative embeddings for speaker verification via channel and spatial attention mechanism in alterable scenarios. Available at SSRN 4178119","key":"16_CR10"},{"doi-asserted-by":"crossref","unstructured":"Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1\u20139 (2015)","key":"16_CR11","DOI":"10.1109\/CVPR.2015.7298594"},{"doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818\u20132826 (2016)","key":"16_CR12","DOI":"10.1109\/CVPR.2016.308"},{"doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","key":"16_CR13","DOI":"10.1109\/CVPR.2016.90"},{"issue":"4","key":"16_CR14","doi-asserted-by":"publisher","first-page":"829","DOI":"10.1007\/s00432-018-02834-7","volume":"145","author":"PR Jeyaraj","year":"2019","unstructured":"Jeyaraj, P.R., Samuel Nadar, E.R.: Computer-assisted medical image classification for early diagnosis of oral cancer employing deep learning algorithm. J. Cancer Res. Clin. Oncol. 145(4), 829\u2013837 (2019)","journal-title":"J. Cancer Res. Clin. Oncol."},{"issue":"7","key":"16_CR15","doi-asserted-by":"publisher","first-page":"1720","DOI":"10.1109\/TMM.2020.2971170","volume":"22","author":"D Gu","year":"2020","unstructured":"Gu, D., et al.: VINet: a visually interpretable image diagnosis network. IEEE Trans. Multimedia 22(7), 1720\u20131729 (2020)","journal-title":"IEEE Trans. Multimedia"},{"key":"16_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.103865","volume":"123","author":"M Graziani","year":"2020","unstructured":"Graziani, M., Andrearczyk, V., Marchand-Maillet, S., M\u00fcller, H.: Concept attribution: explaining CNN decisions to physicians. Comput. Biol. Med. 123, 103865 (2020)","journal-title":"Comput. Biol. Med."},{"doi-asserted-by":"crossref","unstructured":"Villain, E., Mattia, G. M., Nemmi, F., P\u00e9ran, P., Franceries, X., le Lann, M. V.: Visual interpretation of CNN decision-making process using simulated brain MRI. In: 2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS), pp. 515\u2013520 (2021)","key":"16_CR17","DOI":"10.1109\/CBMS52027.2021.00102"}],"container-title":["Lecture Notes in Computer Science","AI 2023: Advances in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-8391-9_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T18:44:51Z","timestamp":1710355491000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8391-9_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,27]]},"ISBN":["9789819983902","9789819983919"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8391-9_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023,11,27]]},"assertion":[{"value":"27 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australasian Joint Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Brisbane, QLD","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 December 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ausai2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ajcai2023.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"213","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"23","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"59","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"11% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}