{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T16:33:15Z","timestamp":1772641995037,"version":"3.50.1"},"reference-count":66,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2024,4,10]],"date-time":"2024-04-10T00:00:00Z","timestamp":1712707200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,4,10]],"date-time":"2024-04-10T00:00:00Z","timestamp":1712707200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100004769","name":"Universit\u00e0 degli Studi di Pavia","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100004769","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"published-print":{"date-parts":[[2024,7]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Hyperspectral imaging is applied in the medical field for automated diagnosis of diseases, especially cancer. Among the various classification algorithms, the most suitable ones are machine and deep learning techniques. In particular, Vision Transformers represent an innovative deep architecture to classify skin cancers through hyperspectral images. However, such methodologies are computationally intensive, requiring parallel solutions to ensure fast classification. In this paper, a parallel Vision Transformer is evaluated exploiting technologies in the context of Edge and Cloud Computing, envisioning portable instruments\u2019 development through the analysis of significant parameters, like processing times, power consumption and communication latency, where applicable. A low-power GPU, different models of desktop GPUs and a GPU for scientific computing were used. Cloud solutions show lower processing times, while Edge boards based on GPU feature the lowest energy consumption, thus resulting as the optimal choice regarding portable instrumentation with no compelling time constraints.<\/jats:p>","DOI":"10.1007\/s11227-024-06076-y","type":"journal-article","created":{"date-parts":[[2024,4,10]],"date-time":"2024-04-10T14:03:34Z","timestamp":1712757814000},"page":"16368-16392","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Edge and cloud computing approaches in the early diagnosis of skin cancer with attention-based vision transformer through hyperspectral imaging"],"prefix":"10.1007","volume":"80","author":[{"given":"Marco","family":"La Salvia","sequence":"first","affiliation":[]},{"given":"Emanuele","family":"Torti","sequence":"additional","affiliation":[]},{"given":"Elisa","family":"Marenzi","sequence":"additional","affiliation":[]},{"given":"Giovanni","family":"Danese","sequence":"additional","affiliation":[]},{"given":"Francesco","family":"Leporati","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,4,10]]},"reference":[{"key":"6076_CR1","doi-asserted-by":"publisher","first-page":"719","DOI":"10.1038\/s41551-018-0305-z","volume":"2","author":"K-H Yu","year":"2018","unstructured":"Yu K-H, Beam AL, Kohane IS (2018) Artificial intelligence in healthcare. Nat Biomed Eng 2:719\u2013731. https:\/\/doi.org\/10.1038\/s41551-018-0305-z","journal-title":"Nat Biomed Eng"},{"key":"6076_CR2","doi-asserted-by":"publisher","first-page":"242","DOI":"10.1016\/j.ejmp.2021.04.016","volume":"83","author":"A Barrag\u00e1n-Montero","year":"2021","unstructured":"Barrag\u00e1n-Montero A, Javaid U, Vald\u00e9s G et al (2021) Artificial intelligence and machine learning for medical imaging: a technology review. Physica Med 83:242\u2013256. https:\/\/doi.org\/10.1016\/j.ejmp.2021.04.016","journal-title":"Physica Med"},{"key":"6076_CR3","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/j.ejmp.2021.02.006","volume":"83","author":"I Castiglioni","year":"2021","unstructured":"Castiglioni I, Rundo L, Codari M et al (2021) AI applications to medical images: from machine learning to deep learning. Phys Med 83:9\u201324. https:\/\/doi.org\/10.1016\/j.ejmp.2021.02.006","journal-title":"Phys Med"},{"key":"6076_CR4","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1016\/j.eng.2019.08.015","volume":"6","author":"G Rong","year":"2020","unstructured":"Rong G, Mendez A, Bou Assi E et al (2020) Artificial intelligence in healthcare: review and prediction case studies. Engineering 6:291\u2013301. https:\/\/doi.org\/10.1016\/j.eng.2019.08.015","journal-title":"Engineering"},{"key":"6076_CR5","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1038\/s41591-021-01614-0","volume":"28","author":"P Rajpurkar","year":"2022","unstructured":"Rajpurkar P, Chen E, Banerjee O, Topol EJ (2022) AI in health and medicine. Nat Med 28:31\u201338. https:\/\/doi.org\/10.1038\/s41591-021-01614-0","journal-title":"Nat Med"},{"key":"6076_CR6","doi-asserted-by":"publisher","first-page":"8485","DOI":"10.1109\/ACCESS.2020.2963939","volume":"8","author":"G Florimbi","year":"2020","unstructured":"Florimbi G, Fabelo H, Torti E et al (2020) Towards real-time computing of intraoperative hyperspectral imaging for brain cancer detection using multi-GPU platforms. IEEE Access 8:8485\u20138501. https:\/\/doi.org\/10.1109\/ACCESS.2020.2963939","journal-title":"IEEE Access"},{"key":"6076_CR7","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1089\/omi.2021.0037","volume":"26","author":"B Lin","year":"2022","unstructured":"Lin B, Wu S (2022) Digital transformation in personalized medicine with artificial intelligence and the internet of medical things. OMICS 26:77\u201381. https:\/\/doi.org\/10.1089\/omi.2021.0037","journal-title":"OMICS"},{"key":"6076_CR8","doi-asserted-by":"crossref","unstructured":"Amsel N, Tomlinson B (2010) Green tracker: a tool for estimating the energy consumption of software. In: CHI \u201910 extended abstracts on human factors in computing systems. ACM, New York, NY, USA, pp 3337\u20133342","DOI":"10.1145\/1753846.1753981"},{"key":"6076_CR9","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-54645-2","volume-title":"Cloud computing","author":"N Antonopoulos","year":"2017","unstructured":"Antonopoulos N, Gillam L (2017) Cloud computing. Springer, Cham"},{"key":"6076_CR10","doi-asserted-by":"publisher","first-page":"1474","DOI":"10.1109\/JPROC.2019.2928287","volume":"107","author":"W Shi","year":"2019","unstructured":"Shi W, Pallis G, Xu Z (2019) Edge computing. Proc IEEE 107:1474\u20131481. https:\/\/doi.org\/10.1109\/JPROC.2019.2928287","journal-title":"Proc IEEE"},{"key":"6076_CR11","doi-asserted-by":"publisher","first-page":"637","DOI":"10.1109\/JIOT.2016.2579198","volume":"3","author":"W Shi","year":"2016","unstructured":"Shi W, Cao J, Zhang Q et al (2016) Edge computing: vision and challenges. IEEE Internet Things J 3:637\u2013646. https:\/\/doi.org\/10.1109\/JIOT.2016.2579198","journal-title":"IEEE Internet Things J"},{"key":"6076_CR12","doi-asserted-by":"crossref","unstructured":"Verma P, Kumar U (2023) Analyzing the application of edge computing in smart healthcare. In: Convergence of cloud with AI for big data analytics. Wiley, pp 121\u2013155","DOI":"10.1002\/9781119905233.ch7"},{"key":"6076_CR13","doi-asserted-by":"publisher","unstructured":"Marenzi E, Torti E, Danese G, Leporati F (2022) FPGA High level synthesis for the classification of skin tumors with hyperspectral images. In: 2022 11th mediterranean conference on embedded computing (MECO). IEEE, pp 1\u20134. https:\/\/doi.org\/10.1109\/MECO55406.2022.9797211","DOI":"10.1109\/MECO55406.2022.9797211"},{"key":"6076_CR14","doi-asserted-by":"publisher","DOI":"10.1007\/s11554-018-0765-0","author":"A Fontanella","year":"2018","unstructured":"Fontanella A, Marenzi E, Torti E et al (2018) A suite of parallel algorithms for efficient band selection from hyperspectral images. J Real Time Image Process. https:\/\/doi.org\/10.1007\/s11554-018-0765-0","journal-title":"J Real Time Image Process"},{"key":"6076_CR15","doi-asserted-by":"publisher","unstructured":"Salvia M La, Torti E, Gazzoni M et al (2022) Attention-based skin cancer classification through hyperspectral imaging. In: 2022 25th euromicro conference on digital system design (DSD). IEEE, pp 871\u2013876. https:\/\/doi.org\/10.1109\/DSD57027.2022.00122","DOI":"10.1109\/DSD57027.2022.00122"},{"key":"6076_CR16","doi-asserted-by":"publisher","first-page":"113000","DOI":"10.1016\/j.rse.2022.113000","volume":"275","author":"JM Meyer","year":"2022","unstructured":"Meyer JM, Kokaly RF, Holley E (2022) Hyperspectral remote sensing of white mica: A review of imaging and point-based spectrometer studies for mineral resources, with spectrometer design considerations. Remote Sens Environ 275:113000. https:\/\/doi.org\/10.1016\/j.rse.2022.113000","journal-title":"Remote Sens Environ"},{"key":"6076_CR17","doi-asserted-by":"publisher","unstructured":"Torti E, Gazzoni M, Marenzi E et al (2023) An attention-based parallel algorithm for hyperspectral skin cancer classification on low-power GPUs. In: 2023 26th Euromicro conference on digital system design (DSD), pp 111\u2013116. https:\/\/doi.org\/10.1109\/DSD60849.2023.00025","DOI":"10.1109\/DSD60849.2023.00025"},{"key":"6076_CR18","doi-asserted-by":"publisher","first-page":"52","DOI":"10.3390\/jimaging5050052","volume":"5","author":"A Signoroni","year":"2019","unstructured":"Signoroni A, Savardi M, Baronio A, Benini S (2019) Deep learning meets hyperspectral image analysis: a multidisciplinary review. J Imaging 5:52. https:\/\/doi.org\/10.3390\/jimaging5050052","journal-title":"J Imaging"},{"key":"6076_CR19","doi-asserted-by":"publisher","first-page":"39","DOI":"10.33969\/JIEC.2020.21004","volume":"2","author":"A Ozdemir","year":"2020","unstructured":"Ozdemir A, Polat K (2020) Deep learning applications for hyperspectral imaging: a systematic review. J Inst Electron Comput 2:39\u201356. https:\/\/doi.org\/10.33969\/JIEC.2020.21004","journal-title":"J Inst Electron Comput"},{"key":"6076_CR20","doi-asserted-by":"publisher","first-page":"012087","DOI":"10.1088\/1742-6596\/1950\/1\/012087","volume":"1950","author":"D Kumar","year":"2021","unstructured":"Kumar D, Kumar D (2021) Hyperspectral image classification using deep learning models: a review. J Phys Conf Ser 1950:012087. https:\/\/doi.org\/10.1088\/1742-6596\/1950\/1\/012087","journal-title":"J Phys Conf Ser"},{"key":"6076_CR21","doi-asserted-by":"publisher","first-page":"102165","DOI":"10.1016\/j.pdpdt.2020.102165","volume":"33","author":"A Rehman","year":"2021","unstructured":"Rehman A, ul Qureshi SA (2021) A review of the medical hyperspectral imaging systems and unmixing algorithms\u2019 in biological tissues. Photodiagnosis Photodyn Ther 33:102165. https:\/\/doi.org\/10.1016\/j.pdpdt.2020.102165","journal-title":"Photodiagnosis Photodyn Ther"},{"key":"6076_CR22","doi-asserted-by":"publisher","first-page":"1503","DOI":"10.3390\/electronics9091503","volume":"9","author":"E Torti","year":"2020","unstructured":"Torti E, Leon R, La Salvia M et al (2020) Parallel classification pipelines for skin cancer detection exploiting hyperspectral imaging on hybrid systems. Electronics 9:1503. https:\/\/doi.org\/10.3390\/electronics9091503","journal-title":"Electronics"},{"key":"6076_CR23","doi-asserted-by":"publisher","first-page":"411","DOI":"10.3390\/electronics7120411","volume":"7","author":"E Torti","year":"2018","unstructured":"Torti E, Fontanella A, Plaza A et al (2018) Hyperspectral image classification using parallel autoencoding diabolo networks on multi-core and many-core architectures. Electronics 7:411. https:\/\/doi.org\/10.3390\/electronics7120411","journal-title":"Electronics"},{"key":"6076_CR24","doi-asserted-by":"publisher","first-page":"7139","DOI":"10.3390\/s22197139","volume":"22","author":"M La Salvia","year":"2022","unstructured":"La Salvia M, Torti E, Leon R et al (2022) Neural networks-based on-site dermatologic diagnosis through hyperspectral epidermal images. Sensors 22:7139. https:\/\/doi.org\/10.3390\/s22197139","journal-title":"Sensors"},{"key":"6076_CR25","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1097\/IIO.0000000000000293","volume":"60","author":"ER Reshef","year":"2020","unstructured":"Reshef ER, Miller JB, Vavvas DG (2020) Hyperspectral imaging of the retina: a review. Int Ophthalmol Clin 60:85\u201396. https:\/\/doi.org\/10.1097\/IIO.0000000000000293","journal-title":"Int Ophthalmol Clin"},{"key":"6076_CR26","doi-asserted-by":"publisher","first-page":"2066","DOI":"10.3390\/diagnostics11112066","volume":"11","author":"M Barberio","year":"2021","unstructured":"Barberio M, Benedicenti S, Pizzicannella M et al (2021) Intraoperative guidance using hyperspectral imaging: a review for surgeons. Diagnostics 11:2066. https:\/\/doi.org\/10.3390\/diagnostics11112066","journal-title":"Diagnostics"},{"key":"6076_CR27","doi-asserted-by":"publisher","first-page":"010901","DOI":"10.1117\/1.JBO.19.1.010901","volume":"19","author":"G Lu","year":"2014","unstructured":"Lu G, Fei B (2014) Medical hyperspectral imaging: a review. J Biomed Opt 19:010901. https:\/\/doi.org\/10.1117\/1.JBO.19.1.010901","journal-title":"J Biomed Opt"},{"key":"6076_CR28","doi-asserted-by":"publisher","first-page":"79534","DOI":"10.1109\/ACCESS.2021.3068392","volume":"9","author":"U Khan","year":"2021","unstructured":"Khan U, Paheding S, Elkin CP, Devabhaktuni VK (2021) Trends in deep learning for medical hyperspectral image analysis. IEEE Access 9:79534\u201379548. https:\/\/doi.org\/10.1109\/ACCESS.2021.3068392","journal-title":"IEEE Access"},{"key":"6076_CR29","doi-asserted-by":"publisher","DOI":"10.1002\/wics.1465","author":"TH Johansen","year":"2020","unstructured":"Johansen TH, M\u00f8llersen K, Ortega S et al (2020) Recent advances in hyperspectral imaging for melanoma detection. WIREs Comput Stat. https:\/\/doi.org\/10.1002\/wics.1465","journal-title":"WIREs Comput Stat"},{"key":"6076_CR30","doi-asserted-by":"publisher","first-page":"1662","DOI":"10.3390\/jcm9061662","volume":"9","author":"R Leon","year":"2020","unstructured":"Leon R, Martinez-Vega B, Fabelo H et al (2020) Non-invasive skin cancer diagnosis using hyperspectral imaging for in-situ clinical support. J Clin Med 9:1662. https:\/\/doi.org\/10.3390\/jcm9061662","journal-title":"J Clin Med"},{"key":"6076_CR31","doi-asserted-by":"publisher","first-page":"6690","DOI":"10.1109\/TGRS.2019.2907932","volume":"57","author":"S Li","year":"2019","unstructured":"Li S, Song W, Fang L et al (2019) Deep learning for hyperspectral image classification: an overview. IEEE Trans Geosci Remote Sens 57:6690\u20136709. https:\/\/doi.org\/10.1109\/TGRS.2019.2907932","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"6076_CR32","doi-asserted-by":"publisher","first-page":"5408","DOI":"10.1109\/TGRS.2018.2815613","volume":"56","author":"X Yang","year":"2018","unstructured":"Yang X, Ye Y, Li X et al (2018) Hyperspectral Image Classification With Deep Learning Models. IEEE Trans Geosci Remote Sens 56:5408\u20135423. https:\/\/doi.org\/10.1109\/TGRS.2018.2815613","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"6076_CR33","doi-asserted-by":"publisher","first-page":"152316","DOI":"10.1109\/ACCESS.2019.2938708","volume":"7","author":"R Lazcano","year":"2019","unstructured":"Lazcano R, Salvador R, Marrero-Martin M et al (2019) Parallel implementations assessment of a spatial-spectral classifier for hyperspectral clinical applications. IEEE Access 7:152316\u2013152333. https:\/\/doi.org\/10.1109\/ACCESS.2019.2938708","journal-title":"IEEE Access"},{"key":"6076_CR34","doi-asserted-by":"publisher","first-page":"3800","DOI":"10.1016\/j.eswa.2011.09.083","volume":"39","author":"GP Petropoulos","year":"2012","unstructured":"Petropoulos GP, Arvanitis K, Sigrimis N (2012) Hyperion hyperspectral imagery analysis combined with machine learning classifiers for land use\/cover mapping. Expert Syst Appl 39:3800\u20133809. https:\/\/doi.org\/10.1016\/j.eswa.2011.09.083","journal-title":"Expert Syst Appl"},{"key":"6076_CR35","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1016\/j.crfs.2021.01.002","volume":"4","author":"D Saha","year":"2021","unstructured":"Saha D, Manickavasagan A (2021) Machine learning techniques for analysis of hyperspectral images to determine quality of food products: a review. Curr Res Food Sci 4:28\u201344. https:\/\/doi.org\/10.1016\/j.crfs.2021.01.002","journal-title":"Curr Res Food Sci"},{"key":"6076_CR36","doi-asserted-by":"crossref","unstructured":"Fabelo H, Ortega S, Kabwama S, et al (2016) HELICoiD project: a new use of hyperspectral imaging for brain cancer detection in real-time during neurosurgical operations. In: Bannon DP (ed), p 986002","DOI":"10.1117\/12.2223075"},{"key":"6076_CR37","doi-asserted-by":"publisher","first-page":"394","DOI":"10.3322\/caac.21492","volume":"68","author":"F Bray","year":"2018","unstructured":"Bray F, Ferlay J, Soerjomataram I et al (2018) Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 68:394\u2013424. https:\/\/doi.org\/10.3322\/caac.21492","journal-title":"CA Cancer J Clin"},{"key":"6076_CR38","doi-asserted-by":"publisher","first-page":"2637","DOI":"10.1007\/s13555-022-00833-8","volume":"12","author":"K Liopyris","year":"2022","unstructured":"Liopyris K, Gregoriou S, Dias J, Stratigos AJ (2022) Artificial intelligence in dermatology: challenges and perspectives. Dermatol Ther 12:2637\u20132651. https:\/\/doi.org\/10.1007\/s13555-022-00833-8","journal-title":"Dermatol Ther"},{"key":"6076_CR39","doi-asserted-by":"publisher","first-page":"1973","DOI":"10.3390\/rs14091973","volume":"14","author":"X Hu","year":"2022","unstructured":"Hu X, Xie C, Fan Z et al (2022) Hyperspectral anomaly detection using deep learning: a review. Remote Sens 14:1973. https:\/\/doi.org\/10.3390\/rs14091973","journal-title":"Remote Sens"},{"key":"6076_CR40","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, et al (2021) An Image is Worth 16x16 words: transformers for image recognition at scale. In: International conference on learning representations"},{"key":"6076_CR41","doi-asserted-by":"publisher","first-page":"2856","DOI":"10.1016\/j.procs.2023.10.278","volume":"225","author":"B Petracchi","year":"2023","unstructured":"Petracchi B, Gazzoni M, Torti E et al (2023) Machine learning-based classification of skin cancer hyperspectral images. Procedia Comput Sci 225:2856\u20132865. https:\/\/doi.org\/10.1016\/j.procs.2023.10.278","journal-title":"Procedia Comput Sci"},{"key":"6076_CR42","doi-asserted-by":"publisher","first-page":"5479","DOI":"10.3390\/ijerph18105479","volume":"18","author":"M Dildar","year":"2021","unstructured":"Dildar M, Akram S, Irfan M et al (2021) Skin cancer detection: a review using deep learning techniques. Int J Environ Res Public Health 18:5479. https:\/\/doi.org\/10.3390\/ijerph18105479","journal-title":"Int J Environ Res Public Health"},{"key":"6076_CR43","doi-asserted-by":"publisher","first-page":"778","DOI":"10.1002\/ijc.33588","volume":"149","author":"J Ferlay","year":"2021","unstructured":"Ferlay J, Colombet M, Soerjomataram I et al (2021) Cancer statistics for the year 2020: AN overview. Int J Cancer 149:778\u2013789. https:\/\/doi.org\/10.1002\/ijc.33588","journal-title":"Int J Cancer"},{"key":"6076_CR44","doi-asserted-by":"publisher","first-page":"7","DOI":"10.3322\/caac.21708","volume":"72","author":"RL Siegel","year":"2022","unstructured":"Siegel RL, Miller KD, Fuchs HE, Jemal A (2022) Cancer statistics, 2022. CA Cancer J Clin 72:7\u201333. https:\/\/doi.org\/10.3322\/caac.21708","journal-title":"CA Cancer J Clin"},{"key":"6076_CR45","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1038\/nature21056","volume":"542","author":"A Esteva","year":"2017","unstructured":"Esteva A, Kuprel B, Novoa RA et al (2017) Dermatologist-level classification of skin cancer with deep neural networks. Nature 542:115\u2013118. https:\/\/doi.org\/10.1038\/nature21056","journal-title":"Nature"},{"key":"6076_CR46","doi-asserted-by":"publisher","first-page":"252","DOI":"10.3390\/s21010252","volume":"21","author":"L Rey-Barroso","year":"2021","unstructured":"Rey-Barroso L, Pe\u00f1a-Guti\u00e9rrez S, Y\u00e1\u00f1ez C et al (2021) Optical technologies for the improvement of skin cancer diagnosis: a review. Sensors 21:252. https:\/\/doi.org\/10.3390\/s21010252","journal-title":"Sensors"},{"key":"6076_CR47","doi-asserted-by":"publisher","first-page":"202","DOI":"10.1016\/j.ejca.2021.06.049","volume":"156","author":"S Haggenm\u00fcller","year":"2021","unstructured":"Haggenm\u00fcller S, Maron RC, Hekler A et al (2021) Skin cancer classification via convolutional neural networks: systematic review of studies involving human experts. Eur J Cancer 156:202\u2013216. https:\/\/doi.org\/10.1016\/j.ejca.2021.06.049","journal-title":"Eur J Cancer"},{"key":"6076_CR48","doi-asserted-by":"crossref","unstructured":"Fabelo H, Melian V, Martinez B, et al (2019) Dermatologic hyperspectral imaging system for skin cancer diagnosis assistance. In: 2019 XXXIV conference on design of circuits and integrated systems (DCIS). IEEE, pp 1\u20136","DOI":"10.1109\/DCIS201949030.2019.8959869"},{"key":"6076_CR49","doi-asserted-by":"publisher","first-page":"8917","DOI":"10.3390\/s22228917","volume":"22","author":"B Martinez-Vega","year":"2022","unstructured":"Martinez-Vega B, Tkachenko M, Matkabi M et al (2022) Evaluation of preprocessing methods on independent medical hyperspectral databases to improve analysis. Sensors 22:8917. https:\/\/doi.org\/10.3390\/s22228917","journal-title":"Sensors"},{"key":"6076_CR50","doi-asserted-by":"crossref","unstructured":"Puthal D, Sahoo BPS, Mishra S, Swain S (2015) Cloud computing features, issues, and challenges: a big picture. In: 2015 international conference on computational intelligence and networks. IEEE, pp 116\u2013123","DOI":"10.1109\/CINE.2015.31"},{"key":"6076_CR51","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1504\/IJCC.2013.050953","volume":"2","author":"P Sasikala","year":"2013","unstructured":"Sasikala P (2013) Research challenges and potential green technological applications in cloud computing. Int J Cloud Comput 2:1. https:\/\/doi.org\/10.1504\/IJCC.2013.050953","journal-title":"Int J Cloud Comput"},{"key":"6076_CR52","doi-asserted-by":"publisher","first-page":"9","DOI":"10.18080\/jtde.v2n3.285","volume":"2","author":"M Zwolenski","year":"2020","unstructured":"Zwolenski M, Weatherill L (2020) The digital universe. J Telecommun Digit Econ 2:9. https:\/\/doi.org\/10.18080\/jtde.v2n3.285","journal-title":"J Telecommun Digit Econ"},{"key":"6076_CR53","doi-asserted-by":"crossref","unstructured":"Kumar U, Verma P, Qamar Abbas S (2021) Bringing edge computing into IoT architecture to improve IoT network performance. In: 2021 international conference on computer communication and informatics (ICCCI). IEEE, pp 1\u20135","DOI":"10.1109\/ICCCI50826.2021.9402499"},{"key":"6076_CR54","unstructured":"(2018) NVIDIA TURING GPU ARCHITECTURE. https:\/\/images.nvidia.com\/aem-dam\/en-zz\/Solutions\/design-visualization\/technologies\/turing-architecture\/NVIDIA-Turing-Architecture-Whitepaper.pdf. Accessed 20 Sep 2023"},{"key":"6076_CR55","unstructured":"(2020) NVIDIA AMPERE GA102 GPU ARCHITECTURE. https:\/\/www.nvidia.com\/content\/PDF\/nvidia-ampere-ga-102-gpu-architecture-whitepaper-v2.pdf. Accessed 20 Sep 2023"},{"key":"6076_CR56","unstructured":"(2022) NVIDIA ADA GPU ARCHITECTURE. https:\/\/images.nvidia.com\/aem-dam\/Solutions\/Data-Center\/l4\/nvidia-ada-gpu-architecture-whitepaper-v2.1.pdf. Accessed 20 Sep 2023"},{"key":"6076_CR57","unstructured":"(2014) NVIDIA Maxwell GM204 Architecture. https:\/\/www.microway.com\/download\/whitepaper\/NVIDIA_Maxwell_GM204_Architecture_Whitepaper.pdf. Accessed 20 Sep 2023"},{"key":"#cr-split#-6076_CR58.1","unstructured":"Spicher N, Schweins M, Thielecke L, et al (2021) Feasibility analysis of fifth-generation"},{"key":"#cr-split#-6076_CR58.2","unstructured":"(5G) mobile networks for transmission of medical imaging data. In: 2021 43rd annual international conference of the IEEE engineering in medicine & biology society (EMBC). IEEE, pp 1791-1795"},{"key":"6076_CR59","doi-asserted-by":"crossref","unstructured":"De Lucia G, Lapegna M, Romano D (2023) A GPU accelerated hyperspectral 3D convolutional neural network classification at\u00a0the\u00a0edge with\u00a0principal component analysis preprocessing, pp 127\u2013138","DOI":"10.1007\/978-3-031-30445-3_11"},{"key":"6076_CR60","doi-asserted-by":"publisher","first-page":"7412","DOI":"10.1109\/JSTARS.2023.3301721","volume":"16","author":"E Torti","year":"2023","unstructured":"Torti E, Marenzi E, Danese G et al (2023) Spatial-spectral feature extraction with local covariance matrix from hyperspectral images through hybrid parallelization. IEEE J Sel Top Appl Earth Obs Remote Sens 16:7412\u20137421. https:\/\/doi.org\/10.1109\/JSTARS.2023.3301721","journal-title":"IEEE J Sel Top Appl Earth Obs Remote Sens"},{"key":"6076_CR61","doi-asserted-by":"publisher","first-page":"4328","DOI":"10.3390\/rs15174328","volume":"15","author":"Z Liu","year":"2023","unstructured":"Liu Z, Han G, Yang H et al (2023) CCC-SSA-UNet: U-shaped pansharpening network with channel cross-concatenation and spatial-spectral attention mechanism for hyperspectral image super-resolution. Remote Sens 15:4328. https:\/\/doi.org\/10.3390\/rs15174328","journal-title":"Remote Sens"},{"key":"6076_CR62","doi-asserted-by":"publisher","unstructured":"Marenzi E, Carrus A, Danese G et al (2017) Efficient parallelization of motion estimation for super-resolution. In: Proceedings\u20142017 25th Euromicro international conference on parallel, distributed and network-based processing, PDP 2017. https:\/\/doi.org\/10.1109\/PDP.2017.64","DOI":"10.1109\/PDP.2017.64"},{"key":"6076_CR63","doi-asserted-by":"publisher","DOI":"10.1109\/TCE.2017.015077","author":"E Marenzi","year":"2017","unstructured":"Marenzi E, Torti E, Leporati F et al (2017) Block matching super-resolution parallel GPU implementation for computational imaging. IEEE Trans Consum Electron. https:\/\/doi.org\/10.1109\/TCE.2017.015077","journal-title":"IEEE Trans Consum Electron"},{"key":"6076_CR64","doi-asserted-by":"publisher","first-page":"1738","DOI":"10.1109\/TETC.2020.3016978","volume":"9","author":"Y Lu","year":"2021","unstructured":"Lu Y, Xie K, Xu G et al (2021) MTFC: A Multi-GPU training framework for cube-CNN-based hyperspectral image classification. IEEE Trans Emerg Top Comput 9:1738\u20131752. https:\/\/doi.org\/10.1109\/TETC.2020.3016978","journal-title":"IEEE Trans Emerg Top Comput"},{"key":"6076_CR65","doi-asserted-by":"crossref","unstructured":"Ordonez A, Heras DB, Arguello F (2022) Multi-GPU registration of high-resolution multispectral images using HSI-KAZE in a cluster system. In: IGARSS 2022\u20142022 IEEE international geoscience and remote sensing symposium. IEEE, pp 5527\u20135530","DOI":"10.1109\/IGARSS46834.2022.9884717"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-024-06076-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-024-06076-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-024-06076-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,25]],"date-time":"2024-06-25T11:03:32Z","timestamp":1719313412000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-024-06076-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,10]]},"references-count":66,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2024,7]]}},"alternative-id":["6076"],"URL":"https:\/\/doi.org\/10.1007\/s11227-024-06076-y","relation":{},"ISSN":["0920-8542","1573-0484"],"issn-type":[{"value":"0920-8542","type":"print"},{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,10]]},"assertion":[{"value":"14 March 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 April 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}