{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T09:26:44Z","timestamp":1776850004658,"version":"3.51.2"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032051813","type":"print"},{"value":"9783032051820","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T00:00:00Z","timestamp":1758153600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T00:00:00Z","timestamp":1758153600000},"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\/978-3-032-05182-0_51","type":"book-chapter","created":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T23:59:24Z","timestamp":1758153564000},"page":"523-532","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["RANDose: A Region-Aware Attention Network for\u00a0Accurate Radiation Dose Prediction"],"prefix":"10.1007","author":[{"given":"G. Jignesh","family":"Chowdary","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tiezhi","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Qian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhaozheng","family":"Yin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,18]]},"reference":[{"issue":"11","key":"51_CR1","doi-asserted-by":"publisher","first-page":"13521","DOI":"10.1007\/s10462-023-10466-8","volume":"56","author":"SF Ahmed","year":"2023","unstructured":"Ahmed, S.F., et al.: Deep learning modelling techniques: current progress, applications, advantages, and challenges. Artif. Intell. Rev. 56(11), 13521\u201313617 (2023)","journal-title":"Artif. Intell. Rev."},{"issue":"10","key":"51_CR2","doi-asserted-by":"publisher","first-page":"1153","DOI":"10.1016\/S1470-2045(15)00222-3","volume":"16","author":"R Atun","year":"2015","unstructured":"Atun, R., et al.: Expanding global access to radiotherapy. Lancet Oncol. 16(10), 1153\u20131186 (2015)","journal-title":"Lancet Oncol."},{"key":"51_CR3","doi-asserted-by":"crossref","unstructured":"Babier, A., et al.: OpenKBP: the open-access knowledge-based planning grand challenge and dataset. Med. Phys. 48(9), 5549\u20135561 (2021)","DOI":"10.1002\/mp.14845"},{"issue":"8","key":"51_CR4","doi-asserted-by":"publisher","first-page":"2939","DOI":"10.1007\/s00371-021-02166-7","volume":"38","author":"K Bayoudh","year":"2022","unstructured":"Bayoudh, K., Knani, R., Hamdaoui, F., Mtibaa, A.: A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets. Vis. Comput. 38(8), 2939\u20132970 (2022)","journal-title":"Vis. Comput."},{"key":"51_CR5","first-page":"100134","volume":"6","author":"J Chai","year":"2021","unstructured":"Chai, J., Zeng, H., Li, A., Ngai, E.W.: Deep learning in computer vision: a critical review of emerging techniques and application scenarios. Mach. Learn. Appl. 6, 100134 (2021)","journal-title":"Mach. Learn. Appl."},{"key":"51_CR6","doi-asserted-by":"crossref","unstructured":"Elyan, E., et al.: Computer vision and machine learning for medical image analysis: recent advances, challenges, and way forward. Artif. Intell. Surg. 2(1), 24\u201345 (2022)","DOI":"10.20517\/ais.2021.15"},{"key":"51_CR7","doi-asserted-by":"publisher","first-page":"106726","DOI":"10.1016\/j.compbiomed.2023.106726","volume":"157","author":"H Jiang","year":"2023","unstructured":"Jiang, H., et al.: A review of deep learning-based multiple-lesion recognition from medical images: classification, detection and segmentation. Comput. Biol. Med. 157, 106726 (2023)","journal-title":"Comput. Biol. Med."},{"key":"51_CR8","doi-asserted-by":"publisher","first-page":"104541","DOI":"10.1016\/j.bspc.2022.104541","volume":"82","author":"F Li","year":"2023","unstructured":"Li, F., Niu, S., Han, Y., Zhang, Y., Dong, Z., Zhu, J.: Multi-stage framework with difficulty-aware learning for progressive dose prediction. Biomed. Signal Process. Control 82, 104541 (2023)","journal-title":"Biomed. Signal Process. Control"},{"key":"51_CR9","doi-asserted-by":"crossref","unstructured":"Lin, Y., Liu, Y., Chen, H., Yang, X., Ma, K., Zheng, Y., Cheng, K.T.: LENAS: learning-based neural architecture search and ensemble for 3-D radiotherapy dose prediction. IEEE Trans. Cybern. (2024)","DOI":"10.1109\/TCYB.2024.3390769"},{"key":"51_CR10","doi-asserted-by":"publisher","first-page":"105580","DOI":"10.1016\/j.compbiomed.2022.105580","volume":"146","author":"D Painuli","year":"2022","unstructured":"Painuli, D., Bhardwaj, S., et al.: Recent advancement in cancer diagnosis using machine learning and deep learning techniques: a comprehensive review. Comput. Biol. Med. 146, 105580 (2022)","journal-title":"Comput. Biol. Med."},{"key":"51_CR11","doi-asserted-by":"crossref","unstructured":"Pramod, A., Naicker, H.S., Tyagi, A.K.: Machine learning and deep learning: open issues and future research directions for the next 10 years. In: Computational Analysis and Deep Learning for Medical Care: Principles, Methods, and Applications, pp. 463\u2013490 (2021)","DOI":"10.1002\/9781119785750.ch18"},{"key":"51_CR12","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"},{"issue":"1","key":"51_CR13","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.gltp.2021.01.004","volume":"2","author":"N Sharma","year":"2021","unstructured":"Sharma, N., Sharma, R., Jindal, N.: Machine learning and deep learning applications-a vision. Glob. Transit. Proc. 2(1), 24\u201328 (2021)","journal-title":"Glob. Transit. Proc."},{"issue":"12","key":"51_CR14","doi-asserted-by":"publisher","first-page":"101649","DOI":"10.1016\/j.adro.2024.101649","volume":"9","author":"G Szalkowski","year":"2024","unstructured":"Szalkowski, G., Xu, X., Das, S., Yap, P.T., Lian, J.: Automatic treatment planning for radiation therapy: a cross-modality and protocol study. Adv. Radiat. Oncol. 9(12), 101649 (2024)","journal-title":"Adv. Radiat. Oncol."},{"issue":"5","key":"51_CR15","doi-asserted-by":"publisher","first-page":"91","DOI":"10.3390\/computers12050091","volume":"12","author":"MM Taye","year":"2023","unstructured":"Taye, M.M.: Understanding of machine learning with deep learning: architectures, workflow, applications and future directions. Computers 12(5), 91 (2023)","journal-title":"Computers"},{"key":"51_CR16","doi-asserted-by":"publisher","unstructured":"Wang, B., et al.: Deep learning-based head and neck radiotherapy planning dose prediction via beam-wise dose decomposition. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022. LNCS, vol. 1343, pp. 575\u2013584. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16449-1_55","DOI":"10.1007\/978-3-031-16449-1_55"},{"key":"51_CR17","doi-asserted-by":"crossref","unstructured":"Xu, X., et al.: Prediction of optimal dosimetry for intensity-modulated radiotherapy with a cascaded auto-content deep learning model. Int. J. Radiat. Oncol. Biol. Phys. 111(3), e113 (2021)","DOI":"10.1016\/j.ijrobp.2021.07.522"},{"issue":"1","key":"51_CR18","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1109\/TCYB.2017.2763682","volume":"49","author":"B Ye","year":"2017","unstructured":"Ye, B., Tang, Q., Yao, J., Gao, W.: Collision-free path planning and delivery sequence optimization in noncoplanar radiation therapy. IEEE Trans. Cybern. 49(1), 42\u201355 (2017)","journal-title":"IEEE Trans. Cybern."},{"issue":"9","key":"51_CR19","doi-asserted-by":"publisher","first-page":"5562","DOI":"10.1002\/mp.14774","volume":"48","author":"L Zimmermann","year":"2021","unstructured":"Zimmermann, L., Faustmann, E., Ramsl, C., Georg, D., Heilemann, G.: Dose prediction for radiation therapy using feature-based losses and one cycle learning. Med. Phys. 48(9), 5562\u20135566 (2021)","journal-title":"Med. Phys."}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2025"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-05182-0_51","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T08:28:00Z","timestamp":1776846480000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-05182-0_51"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,18]]},"ISBN":["9783032051813","9783032051820"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-05182-0_51","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,18]]},"assertion":[{"value":"18 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Daejeon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","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":"23 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}