{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T21:14:05Z","timestamp":1773954845737,"version":"3.50.1"},"publisher-location":"Cham","reference-count":64,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032028129","type":"print"},{"value":"9783032028136","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-02813-6_9","type":"book-chapter","created":{"date-parts":[[2025,8,31]],"date-time":"2025-08-31T07:15:19Z","timestamp":1756624519000},"page":"118-133","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["ODExAI: A Comprehensive Object Detection Explainable AI Evaluation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-4254-0750","authenticated-orcid":false,"given":"Loc Phuc Truong","family":"Nguyen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6750-9536","authenticated-orcid":false,"given":"Hung Truong Thanh","family":"Nguyen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0788-4377","authenticated-orcid":false,"given":"Hung","family":"Cao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,1]]},"reference":[{"key":"9_CR1","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1016\/j.inffus.2021.11.008","volume":"81","author":"L Arras","year":"2022","unstructured":"Arras, L., Osman, A., Samek, W.: CLEVR-XAI: a benchmark dataset for the ground truth evaluation of neural network explanations. Inf. Fusion 81, 14\u201340 (2022). https:\/\/doi.org\/10.1016\/j.inffus.2021.11.008","journal-title":"Inf. Fusion"},{"key":"9_CR2","unstructured":"Arya, V., et\u00a0al.: One explanation does not fit all: a toolkit and taxonomy of AI explainability techniques. arXiv preprint: arXiv:1909.03012 (2019)"},{"key":"9_CR3","unstructured":"Bochkovskiy, A., Wang, C.Y., Liao, H.Y.M.: YOLOv4: optimal speed and accuracy of object detection. arXiv preprint: arXiv:2004.10934 (2020)"},{"issue":"3","key":"9_CR4","doi-asserted-by":"publisher","first-page":"e230074","DOI":"10.1175\/AIES-D-23-0074.1","volume":"3","author":"PL Bommer","year":"2024","unstructured":"Bommer, P.L., Kretschmer, M., Hedstr\u00f6m, A., Bareeva, D., H\u00f6hne, M.M.C.: Finding the right XAI method\u2013a guide for the evaluation and ranking of explainable AI methods in climate science. Artif. Intell. Earth Syst. 3(3), e230074 (2024). https:\/\/doi.org\/10.1175\/AIES-D-23-0074.1","journal-title":"Artif. Intell. Earth Syst."},{"key":"9_CR5","doi-asserted-by":"publisher","first-page":"121530","DOI":"10.1016\/j.foreco.2023.121530","volume":"551","author":"A Buchelt","year":"2024","unstructured":"Buchelt, A., et al.: Exploring artificial intelligence for applications of drones in forest ecology and management. For. Ecol. Manage. 551, 121530 (2024). https:\/\/doi.org\/10.1016\/j.foreco.2023.121530","journal-title":"For. Ecol. Manage."},{"key":"9_CR6","doi-asserted-by":"publisher","unstructured":"C, B., R, S., A, A., Moorthy, R.S.: A novel approach for jute pest detection using improved VGG-19 and XAI. In: 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT), pp.\u00a01\u20135 (2024). https:\/\/doi.org\/10.1109\/ICCCNT61001.2024.10723939","DOI":"10.1109\/ICCCNT61001.2024.10723939"},{"key":"9_CR7","doi-asserted-by":"publisher","unstructured":"Cai, Z., Vasconcelos, N.: Cascade R-CNN: delving into high quality object detection. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6154\u20136162. IEEE Computer Society, Los Alamitos (2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00644","DOI":"10.1109\/CVPR.2018.00644"},{"key":"9_CR8","doi-asserted-by":"publisher","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Computer Vision \u2013 ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part I, pp. 213\u2013229. Springer-Verlag, Berlin, Heidelberg (2020). https:\/\/doi.org\/10.1007\/978-3-030-58452-8_13","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"9_CR9","doi-asserted-by":"publisher","unstructured":"Duan, K., Bai, S., Xie, L., Qi, H., Huang, Q., Tian, Q.: CenterNet: keypoint triplets for object detection. In: 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 6568\u20136577 (2019). https:\/\/doi.org\/10.1109\/ICCV.2019.00667","DOI":"10.1109\/ICCV.2019.00667"},{"key":"9_CR10","doi-asserted-by":"publisher","first-page":"1444763","DOI":"10.3389\/frobt.2024.1444763","volume":"11","author":"M Ennab","year":"2024","unstructured":"Ennab, M., Mcheick, H.: Enhancing interpretability and accuracy of AI models in healthcare: a comprehensive review on challenges and future directions. Front. Robot. AI 11, 1444763 (2024). https:\/\/doi.org\/10.3389\/frobt.2024.1444763","journal-title":"Front. Robot. AI"},{"key":"9_CR11","doi-asserted-by":"publisher","unstructured":"Esmaeili, M., Vettukattil, R., Banitalebi, H., Krogh, N.R., Geitung, J.T.: Explainable artificial intelligence for human-machine interaction in brain tumor localization. J. Personalized Med. 11(11) (2021). https:\/\/doi.org\/10.3390\/jpm11111213","DOI":"10.3390\/jpm11111213"},{"issue":"2","key":"9_CR12","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","volume":"88","author":"M Everingham","year":"2010","unstructured":"Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The pascal visual object classes (VOC) challenge. Int. J. Comput. Vision 88(2), 303\u2013338 (2010). https:\/\/doi.org\/10.1007\/s11263-009-0275-4","journal-title":"Int. J. Comput. Vision"},{"key":"9_CR13","doi-asserted-by":"publisher","first-page":"108042","DOI":"10.1016\/j.compbiomed.2024.108042","volume":"170","author":"L Famiglini","year":"2024","unstructured":"Famiglini, L., Campagner, A., Barandas, M., La Maida, G.A., Gallazzi, E., Cabitza, F.: Evidence-based XAI: an empirical approach to design more effective and explainable decision support systems. Comput. Biol. Med. 170, 108042 (2024). https:\/\/doi.org\/10.1016\/j.compbiomed.2024.108042","journal-title":"Comput. Biol. Med."},{"key":"9_CR14","unstructured":"Ge, Z., Liu, S., Wang, F., Li, Z., Sun, J.: YOLOX: exceeding YOLO series in 2021. arXiv preprint: arXiv:2107.08430 (2021)"},{"key":"9_CR15","doi-asserted-by":"publisher","unstructured":"Gomez, T., Fr\u00e9our, T., Mouch\u00e8re, H.: Metrics for saliency map evaluation of deep learning explanation methods. In: El\u00a0Yacoubi, M., Granger, E., Yuen, P.C., Pal, U., Vincent, N. (eds.) Pattern Recognition and Artificial Intelligence, pp. 84\u201395. Springer International Publishing, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-09037-0_8","DOI":"10.1007\/978-3-031-09037-0_8"},{"key":"9_CR16","doi-asserted-by":"publisher","unstructured":"Grami, A.: The Gaussian Distribution, pp. 201\u2013238. John Wiley & Sons (2019). https:\/\/doi.org\/10.1002\/9781119300847.ch7","DOI":"10.1002\/9781119300847.ch7"},{"key":"9_CR17","doi-asserted-by":"publisher","unstructured":"Gunning, D.: DARPA\u2019s explainable artificial intelligence (XAI) program. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, IUI \u201919, p.\u00a0ii. Association for Computing Machinery, New York (2019). https:\/\/doi.org\/10.1609\/aimag.v40i2.2850","DOI":"10.1609\/aimag.v40i2.2850"},{"key":"9_CR18","doi-asserted-by":"publisher","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.: Mask R-CNN. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 2980\u20132988 (2017). https:\/\/doi.org\/10.1109\/ICCV.2017.322","DOI":"10.1109\/ICCV.2017.322"},{"key":"9_CR19","unstructured":"Hedstr\u00f6m, A., et al.: Quantus: an explainable AI toolkit for responsible evaluation of neural network explanations and beyond. J. Mach. Learn. Res. 24(34), 1\u201311 (2023)"},{"key":"9_CR20","doi-asserted-by":"publisher","unstructured":"Hoofnagle, C.J., van\u00a0der Sloot, B., and, F.Z.B.: The European Union general data protection regulation: what it is and what it means*. Inf. Commun. Technol. Law 28(1), 65\u201398 (2019). https:\/\/doi.org\/10.1080\/13600834.2019.1573501","DOI":"10.1080\/13600834.2019.1573501"},{"key":"9_CR21","doi-asserted-by":"publisher","unstructured":"Kadir, M.A., Mosavi, A., Sonntag, D.: Evaluation metrics for XAI: a review, taxonomy, and practical applications. In: 2023 IEEE 27th International Conference on Intelligent Engineering Systems (INES), pp. 000111\u2013000124 (2023). https:\/\/doi.org\/10.1109\/INES59282.2023.10297629","DOI":"10.1109\/INES59282.2023.10297629"},{"key":"9_CR22","doi-asserted-by":"publisher","first-page":"102520","DOI":"10.1016\/j.jag.2021.102520","volume":"103","author":"I Kakogeorgiou","year":"2021","unstructured":"Kakogeorgiou, I., Karantzalos, K.: Evaluating explainable artificial intelligence methods for multi-label deep learning classification tasks in remote sensing. Int. J. Appl. Earth Obs. Geoinf. 103, 102520 (2021). https:\/\/doi.org\/10.1016\/j.jag.2021.102520","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"issue":"3","key":"9_CR23","doi-asserted-by":"publisher","first-page":"642","DOI":"10.1007\/s11263-019-01204-1","volume":"128","author":"H Law","year":"2019","unstructured":"Law, H., Deng, J.: CornerNet: detecting objects as paired keypoints. Int. J. Comput. Vision 128(3), 642\u2013656 (2019). https:\/\/doi.org\/10.1007\/s11263-019-01204-1","journal-title":"Int. J. Comput. Vision"},{"key":"9_CR24","doi-asserted-by":"publisher","unstructured":"Le, P.Q., Nauta, M., Nguyen, V.B., Pathak, S., Schl\u00f6tterer, J., Seifert, C.: Benchmarking eXplainable AI - a survey on available toolkits and open challenges. In: Elkind, E. (ed.) Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, IJCAI-23, pp. 6665\u20136673. International Joint Conferences on Artificial Intelligence Organization (2023). https:\/\/doi.org\/10.24963\/ijcai.2023\/747, survey Track","DOI":"10.24963\/ijcai.2023\/747"},{"key":"9_CR25","doi-asserted-by":"publisher","unstructured":"Lin, T.Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) Computer Vision \u2013 ECCV 2014, pp. 740\u2013755. Springer International Publishing, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10602-1_48","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"9_CR26","doi-asserted-by":"publisher","unstructured":"Liu, W., et al.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) Computer Vision \u2013 ECCV 2016, pp. 21\u201337. Springer International Publishing, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46448-0_2","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"9_CR27","doi-asserted-by":"publisher","unstructured":"Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows . In: 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 9992\u201310002. IEEE Computer Society, Los Alamitos (2021). https:\/\/doi.org\/10.1109\/ICCV48922.2021.00986","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"9_CR28","doi-asserted-by":"publisher","unstructured":"Lopes, P., Silva, E., Braga, C., Oliveira, T., Rosado, L.: XAI Systems Evaluation: A Review of Human and Computer-Centred Methods. Applied Sciences 12(19) (2022). https:\/\/doi.org\/10.3390\/app12199423","DOI":"10.3390\/app12199423"},{"key":"9_CR29","doi-asserted-by":"publisher","unstructured":"Mohseni, S., Zarei, N., Ragan, E.D.: A multidisciplinary survey and framework for design and evaluation of explainable AI systems. ACM Trans. Interact. Intell. Syst. 11(3\u20134) (2021). https:\/\/doi.org\/10.1145\/3387166","DOI":"10.1145\/3387166"},{"key":"9_CR30","doi-asserted-by":"publisher","unstructured":"Molnar, C., K\u00f6nig, G., et al.: General pitfalls of model-agnostic interpretation methods for machine learning models, pp. 39\u201368. Springer International Publishing, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-04083-2_4","DOI":"10.1007\/978-3-031-04083-2_4"},{"key":"9_CR31","doi-asserted-by":"publisher","first-page":"109183","DOI":"10.1016\/j.engappai.2024.109183","volume":"137","author":"M Moradi","year":"2024","unstructured":"Moradi, M., et al.: Model-agnostic explainable artificial intelligence for object detection in image data. Eng. Appl. Artif. Intell. 137, 109183 (2024). https:\/\/doi.org\/10.1016\/j.engappai.2024.109183","journal-title":"Eng. Appl. Artif. Intell."},{"key":"9_CR32","unstructured":"Mumuni, F., Mumuni, A.: Explainable artificial intelligence (XAI): from inherent explainability to large language models. arXiv preprint: arXiv:2501.09967 (2025)"},{"issue":"1","key":"9_CR33","doi-asserted-by":"publisher","first-page":"13695","DOI":"10.1038\/s41598-024-64601-8","volume":"14","author":"S Natarajan","year":"2024","unstructured":"Natarajan, S., Chakrabarti, P., Margala, M.: Robust diagnosis and meta visualizations of plant diseases through deep neural architecture with explainable AI. Sci. Rep. 14(1), 13695 (2024). https:\/\/doi.org\/10.1038\/s41598-024-64601-8","journal-title":"Sci. Rep."},{"key":"9_CR34","doi-asserted-by":"publisher","unstructured":"Nauta, M., et al.: From anecdotal evidence to quantitative evaluation methods: a systematic review on evaluating explainable AI. ACM Comput. Surv. 55(13s) (2023). https:\/\/doi.org\/10.1145\/3583558","DOI":"10.1145\/3583558"},{"key":"9_CR35","doi-asserted-by":"crossref","unstructured":"Neuwirth, R.: The EU Artificial Intelligence Act: Regulating Subliminal AI Systems. Routledge Research in the Law of Emerging Technologies, Taylor & Francis (2022)","DOI":"10.4324\/9781003319436"},{"key":"9_CR36","doi-asserted-by":"publisher","unstructured":"Nguyen, H., et al.: LangXAI: integrating large vision models for generating textual explanations to enhance explainability in visual perception tasks. In: Larson, K. (ed.) Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, IJCAI-24, pp. 8754\u20138758. International Joint Conferences on Artificial Intelligence Organization (2024). https:\/\/doi.org\/10.24963\/ijcai.2024\/1025, demo Track","DOI":"10.24963\/ijcai.2024\/1025"},{"key":"9_CR37","doi-asserted-by":"crossref","unstructured":"Nguyen, H., et al.: Heart2Mind: human-centered contestable psychiatric disorder diagnosis system using wearable ECG monitors. arXiv preprint: arXiv:2505.11612 (2025)","DOI":"10.1007\/978-981-96-8173-0_33"},{"key":"9_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2024.102782","volume":"116","author":"HTT Nguyen","year":"2025","unstructured":"Nguyen, H.T.T., Nguyen, L.P.T., Cao, H.: XEdgeAI: a human-centered industrial inspection framework with data-centric explainable Edge AI approach. Inf. Fusion 116, 102782 (2025). https:\/\/doi.org\/10.1016\/j.inffus.2024.102782","journal-title":"Inf. Fusion"},{"key":"9_CR39","unstructured":"Nguyen, K., Nguyen, H., Nguyen, K., Truong, B., Phan, T., Cao, H.: Efficient and concise explanations for object detection with gaussian-class activation mapping explainer. In: Proceedings of the Canadian Conference on Artificial Intelligence (2024)"},{"key":"9_CR40","doi-asserted-by":"crossref","unstructured":"Nguyen, T.T.H., Nguyen, V.T.K., Cao, Q.H., Truong, V.B., Nguyen, Q.K., Cao, H.: Enhancing the Fairness and Performance of Edge Cameras with Explainable AI. In: 2024 IEEE International Conference on Consumer Electronics (ICCE), pp.\u00a01\u20134 (2024)","DOI":"10.1109\/ICCE59016.2024.10444383"},{"key":"9_CR41","doi-asserted-by":"crossref","unstructured":"Nguyen, T.T.H., Truong, V.B., Nguyen, V.T.K., Cao, Q.H., Nguyen, Q.K.: Towards trust of explainable AI in thyroid nodule diagnosis. In: International Workshop on Health Intelligence, pp. 11\u201326. Springer (2023)","DOI":"10.1007\/978-3-031-36938-4_2"},{"key":"9_CR42","doi-asserted-by":"publisher","unstructured":"Padmanabhan, D.C., Pl\u00f6ger, P.G., Arriaga, O., Valdenegro-Toro, M.: DExT: detector explanation toolkit. In: Longo, L. (ed.) Explainable Artificial Intelligence, pp. 433\u2013456. Springer Nature Switzerland, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-44067-0_23","DOI":"10.1007\/978-3-031-44067-0_23"},{"key":"9_CR43","unstructured":"Petsiuk, V., Das, A., Saenko, K.: RISE: randomized input sampling for explanation of black-box models. In: British Machine Vision Conference (BMVC) (2018)"},{"key":"9_CR44","doi-asserted-by":"publisher","unstructured":"Petsiuk, V., Jain, R., Manjunatha, V., Morariu, V.I., Mehra, A., Ordonez, V., Saenko, K.: Black-box explanation of object detectors via saliency maps. In: 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11438\u201311447 (2021). https:\/\/doi.org\/10.1109\/CVPR46437.2021.01128","DOI":"10.1109\/CVPR46437.2021.01128"},{"key":"9_CR45","doi-asserted-by":"publisher","unstructured":"Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779\u2013788 (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.91","DOI":"10.1109\/CVPR.2016.91"},{"key":"9_CR46","unstructured":"Redmon, J., Farhadi, A.: YOLOv3: an incremental improvement. arXiv preprint: arXiv:1804.02767 (2018)"},{"key":"9_CR47","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Cortes, C., Lawrence, N., Lee, D., Sugiyama, M., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol.\u00a028. Curran Associates, Inc. (2015)"},{"key":"9_CR48","doi-asserted-by":"publisher","unstructured":"Ribeiro, M.T., Singh, S., Guestrin, C.: \"Why Should I Trust You?\": explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD \u201916, pp. 1135\u20131144. Association for Computing Machinery, New York (2016). https:\/\/doi.org\/10.1145\/2939672.2939778","DOI":"10.1145\/2939672.2939778"},{"issue":"3","key":"9_CR49","doi-asserted-by":"publisher","first-page":"662","DOI":"10.3390\/make3030033","volume":"3","author":"JH Sejr","year":"2021","unstructured":"Sejr, J.H., Schneider-Kamp, P., Ayoub, N.: Surrogate object detection explainer (SODEx) with YOLOv4 and LIME. Mach. Learn. Knowl. Extr. 3(3), 662\u2013671 (2021). https:\/\/doi.org\/10.3390\/make3030033","journal-title":"Mach. Learn. Knowl. Extr."},{"key":"9_CR50","doi-asserted-by":"publisher","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-CAM: visual explanations from deep networks via gradient-based localization. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 618\u2013626 (2017). https:\/\/doi.org\/10.1109\/ICCV.2017.74","DOI":"10.1109\/ICCV.2017.74"},{"key":"9_CR51","doi-asserted-by":"crossref","unstructured":"Song, L., et al.: Explainable artificial intelligence to interpret spatially-explicit impacts of future climate change on species distribution (2024)","DOI":"10.5703\/1288284317811"},{"key":"9_CR52","doi-asserted-by":"publisher","unstructured":"Tahir, H.A., Alayed, W., Hassan, W.U., Haider, A.: A novel hybrid XAI solution for autonomous vehicles: real-time interpretability through LIME\u2013SHAP integration. Sensors 24(21) (2024). https:\/\/doi.org\/10.3390\/s24216776","DOI":"10.3390\/s24216776"},{"issue":"12","key":"9_CR53","doi-asserted-by":"publisher","first-page":"35678","DOI":"10.3934\/math.20241693","volume":"9","author":"K Tarmissi","year":"2024","unstructured":"Tarmissi, K., Mengash, H.A., Negm, N., Said, Y., Al-Sharafi, A.M.: Explainable artificial intelligence with fusion-based transfer learning on adverse weather conditions detection using complex data for autonomous vehicles. AIMS Math. 9(12), 35678\u201335701 (2024). https:\/\/doi.org\/10.3934\/math.20241693","journal-title":"AIMS Math."},{"issue":"6","key":"9_CR54","doi-asserted-by":"publisher","first-page":"2335","DOI":"10.1007\/s00530-022-00960-4","volume":"28","author":"Q Teng","year":"2022","unstructured":"Teng, Q., Liu, Z., Song, Y., Han, K., Lu, Y.: A survey on the interpretability of deep learning in medical diagnosis. Multimedia Syst. 28(6), 2335\u20132355 (2022). https:\/\/doi.org\/10.1007\/s00530-022-00960-4","journal-title":"Multimedia Syst."},{"key":"9_CR55","doi-asserted-by":"publisher","unstructured":"Tian, Z., Shen, C., Chen, H., He, T.: FCOS: fully convolutional one-stage object detection. In: 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 9626\u20139635 (2019). https:\/\/doi.org\/10.1109\/ICCV.2019.00972","DOI":"10.1109\/ICCV.2019.00972"},{"key":"9_CR56","unstructured":"Truong, V.B., Nguyen, T.T.H., Nguyen, V.T.K., Nguyen, Q.K., Cao, Q.H.: Towards better explanations for object detection. In: Yan\u0131ko\u011flu, B., Buntine, W. (eds.) Proceedings of the 15th Asian Conference on Machine Learning. Proceedings of Machine Learning Research, PMLR, vol.\u00a0222, pp. 1385\u20131400 (2024)"},{"key":"9_CR57","doi-asserted-by":"publisher","unstructured":"Wang, H., et al.: Score-CAM: score-weighted visual explanations for convolutional neural networks . In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 111\u2013119. IEEE Computer Society, Los Alamitos (2020). https:\/\/doi.org\/10.1109\/CVPRW50498.2020.00020","DOI":"10.1109\/CVPRW50498.2020.00020"},{"key":"9_CR58","doi-asserted-by":"publisher","first-page":"102391","DOI":"10.1016\/j.media.2022.102391","volume":"78","author":"DA Wood","year":"2022","unstructured":"Wood, D.A., et al.: Deep learning models for triaging hospital head MRI examinations. Med. Image Anal. 78, 102391 (2022). https:\/\/doi.org\/10.1016\/j.media.2022.102391","journal-title":"Med. Image Anal."},{"key":"9_CR59","doi-asserted-by":"crossref","unstructured":"Yamauchi, T.: Spatial sensitive grad-CAM++: improved visual explanation for object detectors via weighted combination of gradient map. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pp. 8164\u20138168 (2024)","DOI":"10.2139\/ssrn.5261383"},{"key":"9_CR60","doi-asserted-by":"publisher","unstructured":"Yamauchi, T., Ishikawa, M.: Spatial sensitive GRAD-CAM: visual explanations for object detection by incorporating spatial sensitivity. In: 2022 IEEE International Conference on Image Processing (ICIP), pp. 256\u2013260 (2022). https:\/\/doi.org\/10.1109\/ICIP46576.2022.9897350","DOI":"10.1109\/ICIP46576.2022.9897350"},{"key":"9_CR61","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1016\/j.inffus.2021.07.016","volume":"77","author":"G Yang","year":"2022","unstructured":"Yang, G., Ye, Q., Xia, J.: Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: a mini-review, two showcases and beyond. Inf. Fusion 77, 29\u201352 (2022). https:\/\/doi.org\/10.1016\/j.inffus.2021.07.016","journal-title":"Inf. Fusion"},{"issue":"10","key":"9_CR62","doi-asserted-by":"publisher","first-page":"1084","DOI":"10.1007\/s11263-017-1059-x","volume":"126","author":"J Zhang","year":"2017","unstructured":"Zhang, J., Bargal, S.A., Lin, Z., Brandt, J., Shen, X., Sclaroff, S.: Top-down neural attention by excitation backprop. Int. J. Comput. Vision 126(10), 1084\u20131102 (2017). https:\/\/doi.org\/10.1007\/s11263-017-1059-x","journal-title":"Int. J. Comput. Vision"},{"key":"9_CR63","doi-asserted-by":"crossref","unstructured":"Zhang, Q., Rao, L., Yang, Y.: Group-CAM: group score-weighted visual explanations for deep convolutional networks. arXiv preprint: arXiv:2103.13859 (2021)","DOI":"10.1109\/CVPRW50498.2020.00020"},{"issue":"9","key":"9_CR64","doi-asserted-by":"publisher","first-page":"5967","DOI":"10.1109\/TPAMI.2024.3380604","volume":"46","author":"C Zhao","year":"2024","unstructured":"Zhao, C., Hsiao, J.H., Chan, A.B.: Gradient-based instance-specific visual explanations for object specification and object discrimination. IEEE Trans. Pattern Anal. Mach. Intell. 46(9), 5967\u20135985 (2024). https:\/\/doi.org\/10.1109\/TPAMI.2024.3380604","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["Lecture Notes in Computer Science","KI 2025: Advances in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-02813-6_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,31]],"date-time":"2025-08-31T07:15:28Z","timestamp":1756624528000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-02813-6_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,1]]},"ISBN":["9783032028129","9783032028136"],"references-count":64,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-02813-6_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,1]]},"assertion":[{"value":"1 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"KI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"German Conference on Artificial Intelligence (K\u00fcnstliche Intelligenz)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Potsdam","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"48","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ki2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ki2025.gi.de\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}