{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T16:02:21Z","timestamp":1777564941229,"version":"3.51.4"},"reference-count":45,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2026]]},"DOI":"10.1109\/access.2026.3660056","type":"journal-article","created":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T20:45:19Z","timestamp":1770065119000},"page":"18099-18114","source":"Crossref","is-referenced-by-count":1,"title":["MEAA-Net: Memory-Efficient Asymmetric Attention for Resource-Constrained Lung Nodule Classification"],"prefix":"10.1109","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-2270-231X","authenticated-orcid":false,"given":"Lan","family":"Qiao","sequence":"first","affiliation":[{"name":"Faculty of Artificial Intelligence, Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Suriayati","family":"Chuprat","sequence":"additional","affiliation":[{"name":"Faculty of Artificial Intelligence, Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21660"},{"key":"ref2","volume-title":"Cancer Facts & Figures 2024","year":"2024"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1056\/nejmoa1102873"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.3389\/fpubh.2023.1264342"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1111\/joim.13480"},{"key":"ref6","article-title":"Medical artificial intelligence for early detection of lung cancer: A survey","author":"Cai","year":"2024","journal-title":"arXiv:2410.14769"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-019-0447-x"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr.2016.90"},{"key":"ref9","first-page":"1","article-title":"An image is worth 16\u00d716 words: Transformers for image recognition at scale","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Dosovitskiy"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1080\/07853890.2024.2405075"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-66179-7_64"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr.2018.00745"},{"key":"ref13","article-title":"Attention U-Net: Learning where to look for the pancreas","author":"Oktay","year":"2018","journal-title":"arXiv:1804.03999"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/iccv48922.2021.00986"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-87193-2_2"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2024.103280"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-25066-8_9"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102299"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.104866"},{"key":"ref21","first-page":"2127","article-title":"Attention-based deep multiple instance learning","volume-title":"Proc. 35th Int. Conf. Mach. Learn. (ICML)","author":"Ilse"},{"key":"ref22","first-page":"2136","article-title":"TransMIL: Transformer based correlated multiple instance learning for whole slide image classification","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","volume":"34","author":"Shao"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr52688.2022.01567"},{"key":"ref24","article-title":"CoAtNet: Marrying convolution and attention for all data sizes","author":"Dai","year":"2021","journal-title":"arXiv:2106.04803"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-20053-3_27"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/tmi.2020.3021387"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/tmi.2024.3405535"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-024-44824-z"},{"key":"ref29","article-title":"VMamba: Visual state space model","author":"Liu","year":"2024","journal-title":"arXiv:2401.10166"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/wacv51458.2022.00181"},{"issue":"147","key":"ref31","first-page":"1","article-title":"Linformer: Self-attention with linear complexity","volume":"22","author":"Wang","year":"2021","journal-title":"J. Mach. Learn. Res."},{"key":"ref32","first-page":"1","article-title":"Rethinking attention with performers","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Choromanski"},{"key":"ref33","first-page":"4203","article-title":"Focal modulation networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","author":"Yang"},{"key":"ref34","article-title":"FlashAttention-2: Faster attention with better parallelism and work partitioning","author":"Dao","year":"2023","journal-title":"arXiv:2307.08691"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.findings-emnlp.372"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-80938-6"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01237-3_12"},{"key":"ref38","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":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr.2018.00286"},{"key":"ref40","article-title":"A white paper on neural network quantization","author":"Nagel","year":"2021","journal-title":"arXiv:2106.08295"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/iccv.2019.00140"},{"key":"ref42","first-page":"6105","article-title":"EfficientNet: Rethinking model scaling for convolutional neural networks","volume-title":"Proc. 36th Int. Conf. Mach. Learn. (ICML)","author":"Tan"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/access.2025.3539122"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2017.06.015"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/tbme.2016.2613502"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/11323511\/11370031.pdf?arnumber=11370031","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T21:08:38Z","timestamp":1770671318000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11370031\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":45,"URL":"https:\/\/doi.org\/10.1109\/access.2026.3660056","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]}}}