{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T21:06:13Z","timestamp":1761253573756,"version":"3.40.3"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030875824"},{"type":"electronic","value":"9783030875831"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-87583-1_19","type":"book-chapter","created":{"date-parts":[[2021,9,22]],"date-time":"2021-09-22T11:37:12Z","timestamp":1632310632000},"page":"191-201","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Adaptable Image Quality Assessment Using Meta-Reinforcement Learning of Task Amenability"],"prefix":"10.1007","author":[{"given":"Shaheer U.","family":"Saeed","sequence":"first","affiliation":[]},{"given":"Yunguan","family":"Fu","sequence":"additional","affiliation":[]},{"given":"Vasilis","family":"Stavrinides","sequence":"additional","affiliation":[]},{"given":"Zachary M. C.","family":"Baum","sequence":"additional","affiliation":[]},{"given":"Qianye","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Mirabela","family":"Rusu","sequence":"additional","affiliation":[]},{"given":"Richard E.","family":"Fan","sequence":"additional","affiliation":[]},{"given":"Geoffrey A.","family":"Sonn","sequence":"additional","affiliation":[]},{"given":"J. Alison","family":"Noble","sequence":"additional","affiliation":[]},{"given":"Dean C.","family":"Barratt","sequence":"additional","affiliation":[]},{"given":"Yipeng","family":"Hu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,9,21]]},"reference":[{"key":"19_CR1","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1016\/j.bspc.2016.02.006","volume":"27","author":"LS Chow","year":"2016","unstructured":"Chow, L.S., Paramesran, R.: Review of medical image quality assessment. Biomed. Sig. Process. Control 27, 145\u2013154 (2016)","journal-title":"Biomed. Sig. Process. Control"},{"issue":"3","key":"19_CR2","doi-asserted-by":"publisher","first-page":"723","DOI":"10.1002\/jmri.25779","volume":"47","author":"SJ Esses","year":"2018","unstructured":"Esses, S.J., et al.: Automated image quality evaluation of T2-weighted liver MRI utilizing deep learning architecture. J. Magn. Reson. Imag. 47(3), 723\u2013728 (2018)","journal-title":"J. Magn. Reson. Imag."},{"key":"19_CR3","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1016\/j.compbiomed.2018.10.004","volume":"103","author":"GT Zago","year":"2018","unstructured":"Zago, G.T., Andre\u00e3o, R.V., Dorizzi, B., Ottoni, E., Salles, T.: Retinal image quality assessment using deep learning. Comput. Biol. Med. 103, 64\u201370 (2018)","journal-title":"Comput. Biol. Med."},{"key":"19_CR4","doi-asserted-by":"publisher","unstructured":"Baum, Z.M.C., et al.: Image quality assessment for closed-loop computer-assisted lung ultrasound. In: Linte, C.A., Siewerdsen, J.H. (eds.) Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling, vol. 11598, pp. 160\u2013166. International Society for Optics and Photonics. SPIE (2021). https:\/\/doi.org\/10.1117\/12.2581865","DOI":"10.1117\/12.2581865"},{"issue":"6","key":"19_CR5","doi-asserted-by":"publisher","first-page":"1221","DOI":"10.1109\/TMI.2017.2690836","volume":"36","author":"AH Abdi","year":"2017","unstructured":"Abdi, A.H., et al.: Automatic quality assessment of echocardiograms using convolutional neural networks: feasibility on the apical four-chamber view. IEEE Trans. Med. Imag. 36(6), 1221\u20131230 (2017). https:\/\/doi.org\/10.1109\/TMI.2017.2690836","journal-title":"IEEE Trans. Med. Imag."},{"issue":"6","key":"19_CR6","doi-asserted-by":"publisher","first-page":"1868","DOI":"10.1109\/TMI.2019.2959209","volume":"39","author":"Z Liao","year":"2019","unstructured":"Liao, Z., et al.: On modelling label uncertainty in deep neural networks: automatic estimation of intra-observer variability in 2D echocardiography quality assessment. IEEE Trans. Med. Imag. 39(6), 1868\u20131883 (2019)","journal-title":"IEEE Trans. Med. Imag."},{"issue":"5","key":"19_CR7","doi-asserted-by":"publisher","first-page":"1336","DOI":"10.1109\/TCYB.2017.2671898","volume":"47","author":"L Wu","year":"2017","unstructured":"Wu, L., Cheng, J., Li, S., Lei, B., Wang, T., Ni, D.: FUIQA: fetal ultrasound image quality assessment with deep convolutional networks. IEEE Trans. Cybern. 47(5), 1336\u20131349 (2017)","journal-title":"IEEE Trans. Cybern."},{"key":"19_CR8","doi-asserted-by":"publisher","unstructured":"Lin, Z., et al.: Multi-task learning for quality assessment of fetal head ultrasound images. Med. Image Anal. 58, 101548 (2019). ISSN: 1361-8415. https:\/\/doi.org\/10.1016\/j.media.2019.101548","DOI":"10.1016\/j.media.2019.101548"},{"key":"19_CR9","doi-asserted-by":"crossref","unstructured":"Davis, H., Russell, S., Barriga, E., Abramoff, M., Soliz, P.: Vision-based, real-time retinal image quality assessment, pp. 1\u20136 (2009)","DOI":"10.1109\/CBMS.2009.5255437"},{"key":"19_CR10","doi-asserted-by":"crossref","unstructured":"K\u00f6hler, T., Budai, A., Kraus, M.F., Odstr\u00e8ilik, J., Michelson, G., Hornegger, J.: Automatic no-reference quality assessment for retinal fundus images using vessel segmentation. In: Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems, pp. 95\u2013100 (2013)","DOI":"10.1109\/CBMS.2013.6627771"},{"key":"19_CR11","doi-asserted-by":"publisher","first-page":"414","DOI":"10.1007\/s11517-006-0045-1","volume":"44","author":"CP Loizou","year":"2006","unstructured":"Loizou, C.P., Pattichis, C.S., Pantziaris, M., Tyllis, T., Nicolaides, A.: Quality evaluation of ultrasound imaging in the carotid artery based on normalization and speckle reduction filtering. Med. Bio. Eng. Comp. 44, 414 (2006)","journal-title":"Med. Bio. Eng. Comp."},{"key":"19_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"755","DOI":"10.1007\/978-3-030-78191-0_58","volume-title":"Information Processing in Medical Imaging","author":"SU Saeed","year":"2021","unstructured":"Saeed, S.U., et al.: Learning image quality assessment by reinforcing task amenable data selection. In: Feragen, A., Sommer, S., Schnabel, J., Nielsen, M. (eds.) IPMI 2021. LNCS, vol. 12729, pp. 755\u2013766. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-78191-0_58"},{"key":"19_CR13","unstructured":"Yoon, J., Arik, S., Pfister, T.: Data Valuation using Reinforcement Learning. arXiv: 1909.11671 (2020)"},{"key":"19_CR14","unstructured":"Duan, Y., Schulman, J., Chen, X., Bartlett, P.L., Sutskever, I., Abbeel, P.: RL2: Fast Reinforcement Learning via Slow Reinforcement Learning. arXiv: 1611.02779 [cs.AI] (2016)"},{"key":"19_CR15","unstructured":"Wang, J.X., et al.: Learning to reinforcement learn. arXiv: 1611.05763 [cs.LG] (2017)"},{"key":"19_CR16","doi-asserted-by":"crossref","unstructured":"Cotter, N.E., Conwell, P.R.: Fixed-weight networks can learn. In: 1990 IJCNN International Joint Conference on Neural Networks, vol. 3, pp. 553\u2013559 (1990)","DOI":"10.1109\/IJCNN.1990.137898"},{"key":"19_CR17","unstructured":"Santoro, A., Bartunov, S., Botvinick, M., Wierstra, D., Lillicrap, T.: Meta-learning with memory-augmented neural networks. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning, vol. 48, pp. 1842\u20131850. Proceedings of Machine Learning Research. PMLR, New York, New York, USA (2016)"},{"issue":"2","key":"19_CR18","doi-asserted-by":"publisher","first-page":"272","DOI":"10.1109\/72.750553","volume":"10","author":"AS Younger","year":"1999","unstructured":"Younger, A.S., Conwell, P.R., Cotter, N.E.: Fixed-weight on-line learning. IEEE Trans. Neural Networks 10(2), 272\u2013283 (1999). https:\/\/doi.org\/10.1109\/72.750553","journal-title":"IEEE Trans. Neural Networks"},{"key":"19_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1007\/3-540-44668-0_13","volume-title":"Artificial Neural Networks \u2014 ICANN 2001","author":"S Hochreiter","year":"2001","unstructured":"Hochreiter, S., Younger, A.S., Conwell, P.R.: Learning to learn using gradient descent. In: Dorffner, G., Bischof, H., Hornik, K. (eds.) ICANN 2001. LNCS, vol. 2130, pp. 87\u201394. Springer, Heidelberg (2001). https:\/\/doi.org\/10.1007\/3-540-44668-0_13"},{"key":"19_CR20","doi-asserted-by":"publisher","unstructured":"Prokhorov, D.V., Feldkarnp, L.A., Tyukin, I.Y.: Adaptive behavior with fixed weights in RNN: an overview. In: Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN 2002, vol. 3, pp. 2018\u20132022 (2002). https:\/\/doi.org\/10.1109\/IJCNN.2002.1007449","DOI":"10.1109\/IJCNN.2002.1007449"},{"issue":"5","key":"19_CR21","doi-asserted-by":"publisher","first-page":"408","DOI":"10.1016\/j.tics.2019.02.006","volume":"23","author":"M Botvinick","year":"2019","unstructured":"Botvinick, M., Ritter, S., Wang, J.X., Kurth-Nelson, Z., Blundell, C., Hassabis, D.: Reinforcement learning, fast and slow. Trends Cogn. Sci. 23(5), 408\u2013422 (2019)","journal-title":"Trends Cogn. Sci."},{"key":"19_CR22","unstructured":"Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms. arXiv: 1707.06347 [cs.LG] (2017)"},{"key":"19_CR23","unstructured":"Nichol, A., Achiam, J., Schulman, J.: On First-Order Meta-Learning Algorithms. arXiv: 1803.02999 [cs.LG] (2018)"},{"key":"19_CR24","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.: ImageNet classification with deep convolutional neural networks. In: NeurIPS (2012)"},{"key":"19_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"}],"container-title":["Lecture Notes in Computer Science","Simplifying Medical Ultrasound"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-87583-1_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,22]],"date-time":"2021-09-22T11:41:48Z","timestamp":1632310908000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-87583-1_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030875824","9783030875831"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-87583-1_19","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"21 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ASMUS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Advances in Simplifying Medical Ultrasound","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Strasbourg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"asmus2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/miccai-ultrasound.github.io\/","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":"OCS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"30","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":"22","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":"73% - 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)"}},{"value":"The conference took place virtually due to the COVID-19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}