{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T17:57:54Z","timestamp":1770746274195,"version":"3.49.0"},"publisher-location":"Cham","reference-count":34,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031377303","type":"print"},{"value":"9783031377310","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-37731-0_17","type":"book-chapter","created":{"date-parts":[[2023,8,9]],"date-time":"2023-08-09T14:02:30Z","timestamp":1691589750000},"page":"216-230","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Comparison of\u00a0Attention Models and\u00a0Post-hoc Explanation Methods for\u00a0Embryo Stage Identification: A Case Study"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0182-4760","authenticated-orcid":false,"given":"Tristan","family":"Gomez","sequence":"first","affiliation":[]},{"given":"Thomas","family":"Fr\u00e9our","sequence":"additional","affiliation":[]},{"given":"Harold","family":"Mouch\u00e8re","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,8,10]]},"reference":[{"key":"17_CR1","doi-asserted-by":"publisher","unstructured":"Afnan, M., et al.: Interpretable, not black-box, artificial intelligence should be used for embryo selection. Human Reprod. Open 2021(4), hoab040 (2021). https:\/\/doi.org\/10.1093\/hropen\/hoab040","DOI":"10.1093\/hropen\/hoab040"},{"key":"17_CR2","doi-asserted-by":"publisher","unstructured":"Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: a next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery (KDD\u201919), pp. 2623\u20132631. Association for Computing Machinery, New York (2019). https:\/\/doi.org\/10.1145\/3292500.3330701","DOI":"10.1145\/3292500.3330701"},{"key":"17_CR3","doi-asserted-by":"publisher","unstructured":"Alqaraawi, A., Schuessler, M., Wei\u00df, P., Costanza, E., Berthouze, N.: Evaluating Saliency Map Explanations for Convolutional Neural Networks: A User Study (IUI \u201920), pp. 275\u2013285. Association for Computing Machinery, New York (2020). https:\/\/doi.org\/10.1145\/3377325.3377519","DOI":"10.1145\/3377325.3377519"},{"key":"17_CR4","doi-asserted-by":"publisher","unstructured":"Bastings, J., Filippova, K.: The elephant in the interpretability room: why use attention as explanation when we have saliency methods? In: Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP, pp. 149\u2013155. Association for Computational Linguistics, Online (2020). https:\/\/doi.org\/10.18653\/v1\/2020.blackboxnlp-1.14","DOI":"10.18653\/v1\/2020.blackboxnlp-1.14"},{"key":"17_CR5","doi-asserted-by":"publisher","unstructured":"Chattopadhay, A., Sarkar, A., Howlader, P., Balasubramanian, V.N.: Grad-cam++: generalized gradient-based visual explanations for deep convolutional networks. In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV) (2018). https:\/\/doi.org\/10.1109\/wacv.2018.00097","DOI":"10.1109\/wacv.2018.00097"},{"key":"17_CR6","unstructured":"Chen, C., Li, O., Barnett, A., Su, J., Rudin, C.: This looks like that: deep learning for interpretable image recognition. In: NeurIPS (2019)"},{"key":"17_CR7","doi-asserted-by":"publisher","unstructured":"Ciray, H.N., et al.: Proposed guidelines on the nomenclature and annotation of dynamic human embryo monitoring by a time-lapse user group. Human Reprod. 29(12), 2650\u20132660 (2014). https:\/\/doi.org\/10.1093\/humrep\/deu278","DOI":"10.1093\/humrep\/deu278"},{"key":"17_CR8","unstructured":"Collobert, R., Kavukcuoglu, K., Farabet, C.: Torch7: a matlab-like environment for machine learning. In: BigLearn, NIPS Workshop (2011)"},{"key":"17_CR9","doi-asserted-by":"publisher","unstructured":"Desai, S., Ramaswamy, H.G.: Ablation-cam: Visual explanations for deep convolutional network via gradient-free localization. In: 2020 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 972\u2013980 (2020). https:\/\/doi.org\/10.1109\/WACV45572.2020.9093360","DOI":"10.1109\/WACV45572.2020.9093360"},{"key":"17_CR10","unstructured":"Dosovitskiy, A., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. In: International Conference on Learning Representations (2021). https:\/\/openreview.net\/forum?id=YicbFdNTTy"},{"key":"17_CR11","doi-asserted-by":"publisher","unstructured":"Fukui, H., Hirakawa, T., Yamashita, T., Fujiyoshi, H.: Attention branch network: learning of attention mechanism for visual explanation. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10697\u201310706 (2019). https:\/\/doi.org\/10.1109\/CVPR.2019.01096","DOI":"10.1109\/CVPR.2019.01096"},{"key":"17_CR12","doi-asserted-by":"publisher","unstructured":"Gomez, T., et al.: A time-lapse embryo dataset for morphokinetic parameter prediction. Data Brief 42 (2022). https:\/\/doi.org\/10.1016\/j.dib.2022.108258","DOI":"10.1016\/j.dib.2022.108258"},{"key":"17_CR13","doi-asserted-by":"publisher","unstructured":"Gomez, T., Fr\u00e9our, T., Mouch\u00e8re, H.: Metrics for saliency map evaluation of deep learning explanation methods. In: International Conference on Pattern Recognition and Artificial Intelligence (2022). https:\/\/doi.org\/10.48550\/ARXIV.2201.13291","DOI":"10.48550\/ARXIV.2201.13291"},{"key":"17_CR14","doi-asserted-by":"publisher","unstructured":"Gomez, T., Ling, S., Fr\u00e9our, T., Mouch\u00e8re, H.: Br-npa: a non-parametric high-resolution attention model to improve the interpretability of attention (2021). https:\/\/doi.org\/10.48550\/ARXIV.2106.02566","DOI":"10.48550\/ARXIV.2106.02566"},{"key":"17_CR15","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"17_CR16","unstructured":"Hu, T., Qi, H.: See better before looking closer: Weakly supervised data augmentation network for fine-grained visual classification. arXiv preprint arXiv:1901.09891 (2019)"},{"key":"17_CR17","doi-asserted-by":"crossref","unstructured":"Huang, Z., Li, Y.: Interpretable and accurate fine-grained recognition via region grouping. In: The IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020)","DOI":"10.1109\/CVPR42600.2020.00869"},{"key":"17_CR18","doi-asserted-by":"publisher","unstructured":"Inhorn, M.C., Patrizio, P.: Infertility around the globe: new thinking on gender, reproductive technologies and global movements in the 21st century. Human Reprod. Update 21(4), 411\u2013426 (2015). https:\/\/doi.org\/10.1093\/humupd\/dmv016","DOI":"10.1093\/humupd\/dmv016"},{"key":"17_CR19","doi-asserted-by":"crossref","unstructured":"Jung, H., Oh, Y.: Towards better explanations of class activation mapping. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 1336\u20131344 (2021)","DOI":"10.1109\/ICCV48922.2021.00137"},{"key":"17_CR20","doi-asserted-by":"crossref","unstructured":"Kendall, M.G.: The treatment of ties in ranking problems. Biometrika 33(3), 239\u2013251 (1945). http:\/\/www.jstor.org\/stable\/2332303","DOI":"10.1093\/biomet\/33.3.239"},{"key":"17_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"339","DOI":"10.1007\/978-3-319-46604-0_25","volume-title":"Computer Vision \u2013 ECCV 2016 Workshops","author":"A Khan","year":"2016","unstructured":"Khan, A., Gould, S., Salzmann, M.: Deep convolutional neural networks for human embryonic cell counting. In: Hua, G., J\u00e9gou, H. (eds.) ECCV 2016. LNCS, vol. 9913, pp. 339\u2013348. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46604-0_25"},{"key":"17_CR22","doi-asserted-by":"publisher","unstructured":"Lin, T.Y., RoyChowdhury, A., Maji, S.: Bilinear CNN models for fine-grained visual recognition. In: Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV) (ICCV \u201915), pp. 1449\u20131457. IEEE Computer Society, Washington, DC (2015). https:\/\/doi.org\/10.1109\/ICCV.2015.170","DOI":"10.1109\/ICCV.2015.170"},{"key":"17_CR23","unstructured":"van\u00a0der Maaten, L., Hinton, G.: Visualizing data using t-sne. J. Mach. Learn. Res. 9(86), 2579\u20132605 (2008). http:\/\/jmlr.org\/papers\/v9\/vandermaaten08a.html"},{"key":"17_CR24","doi-asserted-by":"publisher","unstructured":"McInnes, L., Healy, J., Saul, N., GroSSberger, L.: Umap: Uniform manifold approximation and projection. J. Open Source Softw. 3(29), 861 (2018). https:\/\/doi.org\/10.21105\/joss.00861","DOI":"10.21105\/joss.00861"},{"key":"17_CR25","unstructured":"Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)"},{"key":"17_CR26","unstructured":"Petsiuk, V., Das, A., Saenko, K.: Rise: randomized input sampling for explanation of black-box models. In: BMVC (2018)"},{"key":"17_CR27","doi-asserted-by":"publisher","first-page":"511","DOI":"10.1016\/j.rbmo.2017.06.022","volume":"35","author":"C Pribenszky","year":"2017","unstructured":"Pribenszky, C., Nilselid, A.M., Montag, M.: Time-lapse culture with morphokinetic embryo selection improves pregnancy and live birth chances and reduces early pregnancy loss: a meta-analysis. Reprod. BioMed. Online 35, 511\u2013520 (2017)","journal-title":"Reprod. BioMed. Online"},{"key":"17_CR28","doi-asserted-by":"publisher","unstructured":"Rad, R.M., Saeedi, P., Au, J., Havelock, J.: Blastomere cell counting and centroid localization in microscopic images of human embryo. In: 2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP), pp. 1\u20136 (2018). https:\/\/doi.org\/10.1109\/MMSP.2018.8547107","DOI":"10.1109\/MMSP.2018.8547107"},{"key":"17_CR29","doi-asserted-by":"publisher","unstructured":"Sawada, Y., et al.: Artificial intelligence with attention branch network and deep learning can predict live births by using time-lapse imaging of embryos after in vitro fertilisation. Reprod. BioMed. Online (2021). https:\/\/doi.org\/10.1016\/j.rbmo.2021.05.002","DOI":"10.1016\/j.rbmo.2021.05.002"},{"issue":"2","key":"17_CR30","doi-asserted-by":"publisher","first-page":"336","DOI":"10.1007\/s11263-019-01228-7","volume":"128","author":"RR Selvaraju","year":"2019","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. Int. J. Comput. Vis. 128(2), 336\u2013359 (2019). https:\/\/doi.org\/10.1007\/s11263-019-01228-7","journal-title":"Int. J. Comput. Vis."},{"key":"17_CR31","doi-asserted-by":"crossref","unstructured":"Tsai, C.H., Brusilovsky, P.: Evaluating Visual Explanations for Similarity-Based Recommendations: User Perception and Performance, pp. 22\u201330. Association for Computing Machinery, New York (2019)","DOI":"10.1145\/3320435.3320465"},{"key":"17_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2020.103404","volume":"291","author":"J van der Waa","year":"2021","unstructured":"van der Waa, J., Nieuwburg, E., Cremers, A., Neerincx, M.: Evaluating XAI: a comparison of rule-based and example-based explanations. Artif. Intell. 291, 103404 (2021). https:\/\/doi.org\/10.1016\/j.artint.2020.103404","journal-title":"Artif. Intell."},{"key":"17_CR33","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":"17_CR34","doi-asserted-by":"publisher","unstructured":"Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.319","DOI":"10.1109\/CVPR.2016.319"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-37731-0_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,9]],"date-time":"2023-08-09T14:04:33Z","timestamp":1691589873000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-37731-0_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031377303","9783031377310"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-37731-0_17","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"10 August 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Montr\u00e9al, QC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 August 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 August 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icpr2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iapr.org\/icpr2022","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}