{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T18:45:47Z","timestamp":1761677147450,"version":"3.37.3"},"reference-count":95,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"6","license":[{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Neural Netw. Learning Syst."],"published-print":{"date-parts":[[2024,6]]},"DOI":"10.1109\/tnnls.2022.3230821","type":"journal-article","created":{"date-parts":[[2022,12,29]],"date-time":"2022-12-29T18:35:13Z","timestamp":1672338913000},"page":"8570-8584","source":"Crossref","is-referenced-by-count":9,"title":["MiniSeg: An Extremely Minimum Network Based on Lightweight Multiscale Learning for Efficient COVID-19 Segmentation"],"prefix":"10.1109","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6722-3039","authenticated-orcid":false,"given":"Yu","family":"Qiu","sequence":"first","affiliation":[{"name":"College of Artificial Intelligence, Nankai University, Tianjin, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6143-0264","authenticated-orcid":false,"given":"Yun","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Information Technology and Electrical Engineering, Computer Vision Laboratory, ETH Zürich, Zürich, Switzerland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6288-6984","authenticated-orcid":false,"given":"Shijie","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Information Systems and Artificial Intelligence, University of Bonn, Bonn, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8532-2241","authenticated-orcid":false,"given":"Jing","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Artificial Intelligence, Nankai University, Tianjin, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3127674"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3033146"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.isatra.2021.08.008"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.dsp.2021.103058"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2020.3786"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.20202006.2"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2020200432"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3054306"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3086570"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3070467"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3054746"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.7189\/jogh.10.010347"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1148\/ryct.2020200034"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1148\/ryct.2020204002"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2020200905"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/RBME.2020.2987975"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-020-0824-5"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00272"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3099165"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3111745"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2878849"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3065086"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.3390\/ijerph18063056"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.3390\/healthcare9050522"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2020.2996645"},{"key":"ref26","article-title":"Residual attention U-Net for automated multi-class segmentation of COVID-19 chest CT images","author":"Chen","year":"2020","journal-title":"arXiv:2004.05645"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.104037"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref29","article-title":"MobileNets: Efficient convolutional neural networks for mobile vision applications","author":"Howard","year":"2017","journal-title":"arXiv:1704.04861"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00716"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2699184"},{"key":"ref34","article-title":"Rethinking atrous convolution for semantic image segmentation","author":"Chen","year":"2017","journal-title":"arXiv:1706.05587"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1802.02611"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00388"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01249-6_34"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00941"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.353"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00199"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i6.16617"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.59275\/j.melba.2020-48g7"},{"key":"ref44","article-title":"COVID-CT-dataset: A CT scan dataset about COVID-19","author":"Yang","year":"2020","journal-title":"arXiv:2003.13865"},{"volume-title":"Italian Society of Medical and Interventional Radiology COVID-19 Dataset","year":"2020","author":"Italian","key":"ref45"},{"key":"ref46","article-title":"BIMCV COVID-19+: A large annotated dataset of RX and CT images from COVID-19 patients","author":"Vay\u00e1","year":"2020","journal-title":"arXiv:2006.01174"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3010287"},{"key":"ref48","article-title":"POCOVID-Net: Automatic detection of COVID-19 from a new lung ultrasound imaging dataset (POCUS)","author":"Born","year":"2020","journal-title":"arXiv:2004.12084"},{"key":"ref49","article-title":"SARS-CoV-2 CT-scan dataset: A large dataset of real patients CT scans for SARS-CoV-2 identification","author":"Soares","year":"2020","journal-title":"medRxiv"},{"volume-title":"COVID-19 X-Rays","year":"2020","author":"Dadario","key":"ref50"},{"volume-title":"COVID-19 CT Segmentation Dataset","year":"2020","author":"Jenssen","key":"ref51"},{"key":"ref52","article-title":"COVID-19 CT lung and infection segmentation dataset","volume":"20","author":"Jun","year":"2020","journal-title":"Zenodo"},{"key":"ref53","article-title":"MosMedData: Chest CT scans with COVID-19 related findings","author":"Morozov","year":"2020","journal-title":"arXiv:2005.06465"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2572683"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"ref57","first-page":"1","article-title":"Attention U-Net: Learning where to look for the pancreas","volume-title":"Proc. Comput. Vis. Pattern Recognit.","author":"Oktay"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.178"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2644615"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00747"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00069"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00068"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2019.01.006"},{"key":"ref64","first-page":"1","article-title":"Flattened convolutional neural networks for feedforward acceleration","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Jin"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.5555\/3298023.3298188"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.634"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2020.3042065"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1145\/3338533.3366558"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.2019.8803154"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01219-9_25"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.521"},{"key":"ref72","first-page":"1","article-title":"Deep compression: Compressing deep neural networks with pruning, trained quantization and Huffman coding","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Han"},{"key":"ref73","first-page":"2285","article-title":"Compressing neural networks with the hashing trick","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Chen"},{"issue":"1","key":"ref74","first-page":"6869","article-title":"Quantized neural networks: Training neural networks with low precision weights and activations","volume":"18","author":"Hubara","year":"2017","journal-title":"J. Mach. Learn. Res."},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.98"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298681"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.5555\/3157096.3157329"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1007\/s10044-021-00984-y"},{"key":"ref79","article-title":"Rapid AI development cycle for the coronavirus (COVID-19) pandemic: Initial results for automated detection & patient monitoring using deep learning CT image analysis","author":"Gozes","year":"2020","journal-title":"arXiv:2003.05037"},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.1016\/j.eng.2020.04.010"},{"key":"ref81","article-title":"COVID-19 screening on chest X-ray images using deep learning based anomaly detection","author":"Zhang","year":"2020","journal-title":"arXiv:2003.12338"},{"key":"ref82","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-76550-z"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3082015"},{"key":"ref84","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref85","article-title":"ENet: A deep neural network architecture for real-time semantic segmentation","author":"Paszke","year":"2016","journal-title":"arXiv:1606.02147"},{"key":"ref86","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref87","first-page":"8026","article-title":"PyTorch: An imperative style, high-performance deep learning library","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NIPS)","author":"Paszke"},{"key":"ref88","first-page":"1","article-title":"Adam: A method for stochastic optimization","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Kingma"},{"key":"ref89","first-page":"325","article-title":"BiSeNet: A faster bilateral segmentation network for real-time semantic segmentation","volume-title":"Proc. Int. Joint Conf. Neural Netw. (IJCNN)","author":"Xu"},{"key":"ref90","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-021-01465-9"},{"key":"ref91","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00324"},{"key":"ref92","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00326"},{"key":"ref93","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i07.6805"},{"key":"ref94","first-page":"6105","article-title":"EfficientNet: Rethinking model scaling for convolutional neural networks","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Tan"},{"key":"ref95","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"}],"container-title":["IEEE Transactions on Neural Networks and Learning Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/5962385\/10547160\/10003166.pdf?arnumber=10003166","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,11]],"date-time":"2024-12-11T01:48:53Z","timestamp":1733881733000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10003166\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6]]},"references-count":95,"journal-issue":{"issue":"6"},"URL":"https:\/\/doi.org\/10.1109\/tnnls.2022.3230821","relation":{},"ISSN":["2162-237X","2162-2388"],"issn-type":[{"type":"print","value":"2162-237X"},{"type":"electronic","value":"2162-2388"}],"subject":[],"published":{"date-parts":[[2024,6]]}}}