{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T12:58:46Z","timestamp":1742993926705,"version":"3.40.3"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030313319"},{"type":"electronic","value":"9783030313326"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"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":[[2019]]},"DOI":"10.1007\/978-3-030-31332-6_1","type":"book-chapter","created":{"date-parts":[[2019,9,21]],"date-time":"2019-09-21T15:02:34Z","timestamp":1569078154000},"page":"3-15","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Towards a Joint Approach to Produce Decisions and Explanations Using CNNs"],"prefix":"10.1007","author":[{"given":"Isabel","family":"Rio-Torto","sequence":"first","affiliation":[]},{"given":"Kelwin","family":"Fernandes","sequence":"additional","affiliation":[]},{"given":"Lu\u00eds F.","family":"Teixeira","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,9,22]]},"reference":[{"key":"1_CR1","unstructured":"Alber, M., et al.: iNNvestigate neural networks! (2018)"},{"issue":"7","key":"1_CR2","doi-asserted-by":"publisher","first-page":"e0130140","DOI":"10.1371\/journal.pone.0130140","volume":"10","author":"Sebastian Bach","year":"2015","unstructured":"Bach, S., Binder, A., Montavon, G., Klauschen, F., M\u00fcller, K.R., Samek, W.: On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PLoS One (2015). https:\/\/doi.org\/10.1371\/journal.pone.0130140","journal-title":"PLOS ONE"},{"key":"1_CR3","unstructured":"Geirhos, R., Rubisch, P., Michaelis, C., Bethge, M., Wichmann, F.A., Brendel, W.: ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness, November 2018. http:\/\/arxiv.org\/abs\/1811.12231"},{"key":"1_CR4","doi-asserted-by":"publisher","unstructured":"Gilpin, L.H., Bau, D., Yuan, B.Z., Bajwa, A., Specter, M., Kagal, L.: Explaining explanations: an overview of interpretability of machine learning. In: Proceedings of the 2018 IEEE 5th International Conference on Data Science and Advanced Analytics DSAA 2018, pp. 80\u201389 (2019). https:\/\/doi.org\/10.1109\/DSAA.2018.00018","DOI":"10.1109\/DSAA.2018.00018"},{"issue":"3","key":"1_CR5","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1609\/aimag.v38i3.2741","volume":"38","author":"Bryce Goodman","year":"2017","unstructured":"Goodman, B., Flaxman, S.: European Union regulations on algorithmic decision-making and a \u201cright to explanation\u201d, June 2016. https:\/\/doi.org\/10.1609\/aimag.v38i3.2741, https:\/\/arxiv.org\/abs\/1606.08813","journal-title":"AI Magazine"},{"issue":"10","key":"1_CR6","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1145\/3233231","volume":"61","author":"Zachary C. Lipton","year":"2018","unstructured":"Lipton, Z.C.: The Mythos of Model Interpretability (2016). https:\/\/doi.org\/10.1145\/3233231","journal-title":"Communications of the ACM"},{"key":"1_CR7","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1016\/j.patcog.2016.11.008","volume":"65","author":"Gr\u00e9goire Montavon","year":"2017","unstructured":"Montavon, G., Lapuschkin, S., Binder, A., Samek, W., M\u00fcller, K.R.: Explaining nonlinear classification decisions with deep Taylor decomposition. Pattern Recognit. (2017). https:\/\/doi.org\/10.1016\/j.patcog.2016.11.008","journal-title":"Pattern Recognition"},{"key":"1_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.dsp.2017.10.011","volume":"73","author":"Gr\u00e9goire Montavon","year":"2018","unstructured":"Montavon, G., Samek, W., M\u00fcller, K.R.: Methods for interpreting and understanding deep neural networks. Digit. Signal Process. 73, 1\u201315 (2018). https:\/\/doi.org\/10.1016\/J.DSP.2017.10.011. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1051200417302385","journal-title":"Digital Signal Processing"},{"key":"1_CR9","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Singh, S., Guestrin, C.: \u201cWhy Should I Trust You? Explaining the Predictions of Any Classifier, February 2016. http:\/\/arxiv.org\/abs\/1602.04938","DOI":"10.1145\/2939672.2939778"},{"issue":"11","key":"1_CR10","doi-asserted-by":"publisher","first-page":"2660","DOI":"10.1109\/TNNLS.2016.2599820","volume":"28","author":"W Samek","year":"2017","unstructured":"Samek, W., Binder, A., Montavon, G., Lapuschkin, S., M\u00fcller, K.R.: Evaluating the visualization of what a deep neural network has learned. IEEE Trans. Neural Netw. Learn. Syst. 28(11), 2660\u20132673 (2017)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"1_CR11","doi-asserted-by":"crossref","unstructured":"Silva, W., Fernandes, K., Cardoso, J.S.: How to produce complementary explanations using an ensemble model. In: 2019 International Joint Conference on Neural Networks (IJCNN) (2019)","DOI":"10.1109\/IJCNN.2019.8852409"},{"key":"1_CR12","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1007\/978-3-030-02628-8_15","volume-title":"Understanding and Interpreting Machine Learning in Medical Image Computing Applications","author":"Wilson Silva","year":"2018","unstructured":"Silva, W., Fernandes, K., Cardoso, M.J., Cardoso, J.S.: Towards Complementary Explanations Using Deep Neural Networks (2018). https:\/\/doi.org\/10.1007\/978-3-030-02628-8_15"},{"key":"1_CR13","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"1_CR14","unstructured":"Smilkov, D., Thorat, N., Kim, B., Vi, F.: SmoothGrad: removing noise by adding noise (2017)"},{"key":"1_CR15","unstructured":"Springenberg, J.T., Dosovitskiy, A., Brox, T., Riedmiller, M.: Striving for Simplicity: The All Convolutional Net, December 2014. https:\/\/arxiv.org\/abs\/1412.6806"},{"key":"1_CR16","unstructured":"Zeiler, M.D.: ADADELTA: an adaptive learning rate method. arXiv preprint arXiv:1212.5701 (2012)"},{"key":"1_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"818","DOI":"10.1007\/978-3-319-10590-1_53","volume-title":"Computer Vision \u2013 ECCV 2014","author":"MD Zeiler","year":"2014","unstructured":"Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 818\u2013833. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10590-1_53"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Image Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-31332-6_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T00:06:47Z","timestamp":1695254807000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-31332-6_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030313319","9783030313326"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-31332-6_1","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"22 September 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IbPRIA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Iberian Conference on Pattern Recognition and Image Analysis","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Madrid","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 July 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 July 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ibpria2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ibpria.org\/2019\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-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":"137","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":"99","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":"0","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":"72% - 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":"3.1","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":"4","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)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}