{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T05:33:06Z","timestamp":1763616786406,"version":"3.45.0"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783032006516"},{"type":"electronic","value":"9783032006523"}],"license":[{"start":{"date-parts":[[2025,8,20]],"date-time":"2025-08-20T00:00:00Z","timestamp":1755648000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,8,20]],"date-time":"2025-08-20T00:00:00Z","timestamp":1755648000000},"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\/978-3-032-00652-3_7","type":"book-chapter","created":{"date-parts":[[2025,8,19]],"date-time":"2025-08-19T12:59:19Z","timestamp":1755608359000},"page":"82-95","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Knowledge Distillation for\u00a0Computationally Tractable Brain Tumour Segmentation in\u00a0Sub-saharan Africa"],"prefix":"10.1007","author":[{"given":"Gage","family":"Nott","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9040-3601","authenticated-orcid":false,"given":"Hima","family":"Vadapalli","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5632-1220","authenticated-orcid":false,"given":"Dustin van","family":"der Haar","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,8,20]]},"reference":[{"key":"7_CR1","unstructured":"Adewole, M., et al.: The brain tumor segmentation (BraTS) challenge 2023: glioma segmentation in sub-Saharan Africa Patient Population (BraTS-Africa). ArXiv (2023)"},{"issue":"7","key":"7_CR2","first-page":"3523","volume":"44","author":"S Minaee","year":"2022","unstructured":"Minaee, S., Boykov, Y., Porikli, F., Plaza, A., Kehtarnavaz, N., Terzopoulos, D.: Image segmentation using deep learning: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 44(7), 3523\u20133542 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"8","key":"7_CR3","doi-asserted-by":"publisher","first-page":"1871","DOI":"10.1109\/TBME.2017.2783305","volume":"65","author":"V Cherukuri","year":"2018","unstructured":"Cherukuri, V., Ssenyonga, P., Warf, B.C., Kulkarni, A.V., Monga, V., Schiff, S.J.: Learning based segmentation of CT brain images: application to postoperative hydrocephalic scans. IEEE Trans. Biomed. Eng. 65(8), 1871\u20131884 (2018)","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"5","key":"7_CR4","doi-asserted-by":"publisher","first-page":"1243","DOI":"10.1049\/ipr2.12419","volume":"16","author":"R Wang","year":"2022","unstructured":"Wang, R., Lei, T., Cui, R., Zhang, B., Meng, H., Nandi, A.K.: Medical image segmentation using deep learning: a survey. IET Image Proc. 16(5), 1243\u20131267 (2022)","journal-title":"IET Image Proc."},{"key":"7_CR5","doi-asserted-by":"crossref","unstructured":"Ghosh, A., Thakur, S.: Review of brain tumor MRI image segmentation methods for BraTS challenge dataset, pp. 405-410 (2022)","DOI":"10.1109\/Confluence52989.2022.9734134"},{"key":"7_CR6","unstructured":"Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2015)"},{"issue":"12","key":"7_CR7","doi-asserted-by":"publisher","first-page":"3820","DOI":"10.1109\/TMI.2021.3098703","volume":"40","author":"D Qin","year":"2021","unstructured":"Qin, D., et al.: Efficient medical image segmentation based on knowledge distillation. IEEE Trans. Med. Imaging 40(12), 3820\u20133831 (2021)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"1","key":"7_CR8","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1109\/TPDS.2021.3084813","volume":"33","author":"G Lu","year":"2022","unstructured":"Lu, G., Zhang, W., Wang, Z.: Optimizing depthwise separable convolution operations on GPUs. IEEE Trans. Parallel Distrib. Syst. 33(1), 70\u201387 (2022)","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"7_CR9","unstructured":"Howard, A.G., MobileNets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)"},{"key":"7_CR10","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.-C.: Mobilenetv2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018)","DOI":"10.1109\/CVPR.2018.00474"},{"key":"7_CR11","doi-asserted-by":"crossref","unstructured":"Kanadath, A., Jothi, J.A.A., Urolagin, S.: Histopathology image segmentation using mobilenetV2 based U-net model. In: 2021 International Conference on Intelligent Technologies (CONIT) (2021)","DOI":"10.1109\/CONIT51480.2021.9498341"},{"key":"7_CR12","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58577-8_9","volume-title":"Improving Face Recognition from Hard Samples via Distribution Distillation Loss","author":"Y Huang","year":"2020","unstructured":"Huang, Y., et al.: Improving Face Recognition from Hard Samples via Distribution Distillation Loss. Springer International Publishing, Cham (2020)"},{"key":"7_CR13","doi-asserted-by":"crossref","unstructured":"Park, W., Kim, D., Lu, Y., Cho,M.: Relational knowledge distillation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2019)","DOI":"10.1109\/CVPR.2019.00409"},{"key":"7_CR14","doi-asserted-by":"crossref","unstructured":"Ren, T., Honey,E., Rebala, H., Sharma, A., Chopra, A., Kurt, M.: An optimization framework for processing and transfer learning for the brain tumor segmentation. ArXiv (2024). abs\/2402.07008","DOI":"10.1007\/978-3-031-76163-8_15"},{"key":"7_CR15","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-09002-8_2","volume-title":"Optimized U-Net for Brain Tumor Segmentation","author":"M Futrega","year":"2022","unstructured":"Futrega, M., Milesi, A., Marcinkiewicz, M., Ribalta, P.: Optimized U-Net for Brain Tumor Segmentation. Springer International Publishing, Cham (2022)"},{"key":"7_CR16","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) (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"7_CR17","unstructured":"Silversmith, W.: Connected-Components-3D 1.13. 0 (2024)"},{"key":"7_CR18","doi-asserted-by":"crossref","unstructured":"Zhao, R., et al.: Rethinking dice loss for medical image segmentation. In: 2020 IEEE International Conference on Data Mining (ICDM) (2020)","DOI":"10.1109\/ICDM50108.2020.00094"},{"key":"7_CR19","unstructured":"Raspberry Pi. Raspberry Pi 3 Model B (2024). [ Accessed 23 Agu 2024]. https:\/\/www.raspberrypi.com\/products\/raspberry-pi-3-model-b\/"},{"key":"7_CR20","unstructured":"Parida, A., et al.: Adult glioma segmentation in sub-Saharan Africa using transfer learning on stratified finetuning data. arXiv:2412.04111 [eess.IV] (2024). https:\/\/doi.org\/10.48550\/arXiv.2412.04111"},{"key":"7_CR21","unstructured":"Mukhoti, J., Kulharia, V., Sanyal, A., Golodetz, S., Torr, P., Dokania, P.: Calibrating deep neural networks using focal loss. Adv. Neural Info. Process. Syst. 33, 15288\u201315299 (2020)"},{"key":"7_CR22","doi-asserted-by":"crossref","unstructured":"Yang, G., Yu, S., Sheng, Y., et al.: Attention and feature transfer based knowledge distillation. Sci. Rep. 13, 18369 (2023)","DOI":"10.1038\/s41598-023-43986-y"},{"key":"7_CR23","unstructured":"Nigjeh, M.K.: Precision Meets Efficiency: Optimizing Multimodal MRI Brain Tumor Segmentation via Lightweight Encoders and Model Pruning. Master\u2019s thesis, Southern Illinois University at Edwardsville (2025)"}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence in Healthcare"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-00652-3_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T04:51:41Z","timestamp":1763614301000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-00652-3_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,20]]},"ISBN":["9783032006516","9783032006523"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-00652-3_7","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025,8,20]]},"assertion":[{"value":"20 August 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The author has no competing interests to declare relevant to this article\u2019s content.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"AIiH","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on AI in Healthcare","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Cambridge","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","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":"8 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 September 2025","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":"aiih2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/aiih.cc\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}