{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T14:57:25Z","timestamp":1791212245761,"version":"4.1.0"},"reference-count":47,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100003787","name":"Natural Science Foundation of Hebei Province","doi-asserted-by":"publisher","award":["F2025404013"],"award-info":[{"award-number":["F2025404013"]}],"id":[{"id":"10.13039\/501100003787","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62001255"],"award-info":[{"award-number":["62001255"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62262048"],"award-info":[{"award-number":["62262048"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004763","name":"Inner Mongolia Autonomous Region Natural Science Foundation","doi-asserted-by":"publisher","award":["2024MS06008"],"award-info":[{"award-number":["2024MS06008"]}],"id":[{"id":"10.13039\/501100004763","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004763","name":"Inner Mongolia Autonomous Region Natural Science Foundation","doi-asserted-by":"publisher","award":["2019MS06003"],"award-info":[{"award-number":["2019MS06003"]}],"id":[{"id":"10.13039\/501100004763","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004763","name":"Inner Mongolia Autonomous Region Natural Science Foundation","doi-asserted-by":"publisher","award":["2025ZD008"],"award-info":[{"award-number":["2025ZD008"]}],"id":[{"id":"10.13039\/501100004763","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computerized Medical Imaging and Graphics"],"published-print":{"date-parts":[[2026,10]]},"DOI":"10.1016\/j.compmedimag.2026.102820","type":"journal-article","created":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T06:42:19Z","timestamp":1788417739000},"page":"102820","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["DARTS-searched feedback-augmented state space model for 3D pulmonary nodule classification"],"prefix":"10.1016","volume":"135","author":[{"given":"Junjie","family":"Cui","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4345-6932","authenticated-orcid":false,"given":"Yu","family":"Gu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mansheng","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meng","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lidong","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Baohua","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianjun","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juan","family":"Hao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siyuan","family":"Tang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qun","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.compmedimag.2026.102820_bib1","doi-asserted-by":"crossref","first-page":"3257","DOI":"10.1038\/s41598-025-34569-0","article-title":"AI driven hybrid convolutional and transformer based deep learning architecture for precise lung nodule classification","volume":"16","author":"Abdullah","year":"2026","journal-title":"Sci. Rep."},{"key":"10.1016\/j.compmedimag.2026.102820_bib2","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1590\/s1806-37562017000000259","article-title":"CT-guided percutaneous core needle biopsy of pulmonary nodules smaller than 2 cm: technical aspects and factors influencing accuracy","volume":"44","author":"Andrade","year":"2018","journal-title":"J. Bras. Pneumol."},{"key":"10.1016\/j.compmedimag.2026.102820_bib3","doi-asserted-by":"crossref","first-page":"915","DOI":"10.1118\/1.3528204","article-title":"The lung image database consortium (LIDC) and image database resource initiative (IDRI): A completed reference database of lung nodules on CT scans","volume":"38","author":"Armato","year":"2011","journal-title":"Med. Phys."},{"key":"10.1016\/j.compmedimag.2026.102820_bib4","doi-asserted-by":"crossref","DOI":"10.1016\/j.compmedimag.2026.102713","article-title":"Differentiable neural architecture search for medical image segmentation: a systematic review and field audit","volume":"128","author":"Benedykciuk","year":"2026","journal-title":"Comput. Med. Imaging Graph."},{"key":"10.1016\/j.compmedimag.2026.102820_bib5","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2024.109999","article-title":"Mamba-enhanced spectral-attentive wavelet network for underwater image restoration","volume":"143","author":"Chang","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.compmedimag.2026.102820_bib6","first-page":"1294","article-title":"Progressive differentiable architecture search: Bridging the depth gap between search and evaluation","author":"Chen","year":"2019","journal-title":"Presente Proc. IEEE\/CVF Int. Conf. Comput. Vision."},{"key":"10.1016\/j.compmedimag.2026.102820_bib7","series-title":"Presented at the European Conference on Computer Vision","first-page":"465","article-title":"Fair DARTS: Eliminating unfair advantages in differentiable architecture search","author":"Chu","year":"2020"},{"key":"10.1016\/j.compmedimag.2026.102820_bib8","series-title":"Neural Computing for Advanced Applications, Communications in Computer and Information Science","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1007\/978-981-95-3736-5_15","article-title":"Neural architecture search for medical image classification via latent space and evolutionary optimization","author":"Dai","year":"2025"},{"key":"10.1016\/j.compmedimag.2026.102820_bib9","article-title":"An image is worth 16x16 words: transformers for image recognition at scale","author":"Dosovitskiy","year":"2021","journal-title":"Presente Int. Conf. Learn. Represent."},{"key":"10.1016\/j.compmedimag.2026.102820_bib10","article-title":"Mamba: linear-time sequence modeling with selective state spaces","author":"Gu","year":"2024","journal-title":"Presente First Conf. Lang. Model."},{"key":"10.1016\/j.compmedimag.2026.102820_bib11","first-page":"1474","article-title":"HiPPO: Recurrent memory with optimal polynomial projections","volume":"33","author":"Gu","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.compmedimag.2026.102820_bib12","first-page":"1","article-title":"Efficiently modeling long sequences with structured state spaces","author":"Gu","year":"2022","journal-title":"Int. Conf. Learn. Represent."},{"key":"10.1016\/j.compmedimag.2026.102820_bib13","doi-asserted-by":"crossref","DOI":"10.1002\/ima.23176","article-title":"SSANet\u2014novel residual network for computer-aided diagnosis of pulmonary nodules in chest computed tomography","volume":"34","author":"Gu","year":"2024","journal-title":"Int. J. Imaging Syst. Technol."},{"key":"10.1016\/j.compmedimag.2026.102820_bib14","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016","journal-title":"Presente Proc. IEEE Conf. Comput. Vision. Pattern Recognit."},{"key":"10.1016\/j.compmedimag.2026.102820_bib15","doi-asserted-by":"crossref","DOI":"10.1117\/1.JEI.30.1.013012","article-title":"A-DARTS: attention-guided differentiable architecture search for lung nodule classification","volume":"30","author":"Hu","year":"2021","journal-title":"J. Electron. Imaging"},{"key":"10.1016\/j.compmedimag.2026.102820_bib16","doi-asserted-by":"crossref","first-page":"422","DOI":"10.1016\/j.neucom.2019.03.103","article-title":"Attentive and ensemble 3D dual path networks for pulmonary nodules classification","volume":"398","author":"Jiang","year":"2020","journal-title":"Neurocomputing"},{"key":"10.1016\/j.compmedimag.2026.102820_bib17","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2021.107825","article-title":"Learning efficient, explainable and discriminative representations for pulmonary nodules classification","volume":"113","author":"Jiang","year":"2021","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.compmedimag.2026.102820_bib18","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1137\/0301010","article-title":"Mathematical description of linear dynamical systems","volume":"1","author":"Kalman","year":"1963","journal-title":"J. Soc. Ind. Appl. Math."},{"key":"10.1016\/j.compmedimag.2026.102820_bib19","doi-asserted-by":"crossref","DOI":"10.1016\/j.compmedimag.2025.102595","article-title":"CT-mamba: A hybrid convolutional state space model for low-dose CT denoising","volume":"124","author":"Li","year":"2025","journal-title":"Comput. Med. Imaging Graph."},{"key":"10.1016\/j.compmedimag.2026.102820_bib20","article-title":"DARTS: differentiable architecture search","author":"Liu","year":"2019","journal-title":"Presente Int. Conf. Learn. Represent."},{"key":"10.1016\/j.compmedimag.2026.102820_bib21","series-title":"Advances in Neural Information Processing Systems 37. Presented at the Advances in Neural Information Processing Systems 37","first-page":"103031","article-title":"VMamba: Visual state space model","author":"Liu","year":"2024"},{"key":"10.1016\/j.compmedimag.2026.102820_bib22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.media.2017.06.015","article-title":"Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge","volume":"42","author":"Setio","year":"2017","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.compmedimag.2026.102820_bib23","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1016\/j.neunet.2023.11.006","article-title":"A survey on cancer detection via convolutional neural networks: Current challenges and future directions","volume":"169","author":"Sharma","year":"2024","journal-title":"Neural Netw."},{"key":"10.1016\/j.compmedimag.2026.102820_bib24","doi-asserted-by":"crossref","first-page":"663","DOI":"10.1016\/j.patcog.2016.05.029","article-title":"Multi-crop convolutional neural networks for lung nodule malignancy suspiciousness classification","volume":"61","author":"Shen","year":"2017","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.compmedimag.2026.102820_bib25","first-page":"12","article-title":"Cancer statistics, 2024","volume":"74","author":"Siegel","year":"2024","journal-title":"Ca. Cancer J. Clin."},{"key":"10.1016\/j.compmedimag.2026.102820_bib26","doi-asserted-by":"crossref","first-page":"21","DOI":"10.3390\/diseases14010021","article-title":"Segmentation-guided hybrid deep learning for pulmonary nodule detection and risk prediction from multi-cohort CT images","volume":"14","author":"Subramanyam","year":"2026","journal-title":"Diseases"},{"key":"10.1016\/j.compmedimag.2026.102820_bib27","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.107597","article-title":"MEDS-net: Multi-encoder based self-distilled network with bidirectional maximum intensity projections fusion for lung nodule detection","volume":"129","author":"Usman","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.compmedimag.2026.102820_bib28","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"van der Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.compmedimag.2026.102820_bib29","first-page":"5998","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.compmedimag.2026.102820_bib30","doi-asserted-by":"crossref","DOI":"10.1049\/cmu2.70052","article-title":"AMCF-net: A novel adaptive multi-channel fusion network for computer-aided diagnosis of lung nodules in chest computed tomography","volume":"19","author":"Wang","year":"2025","journal-title":"IET Commun."},{"key":"10.1016\/j.compmedimag.2026.102820_bib31","doi-asserted-by":"crossref","first-page":"738","DOI":"10.1109\/TIP.2023.3349004","article-title":"TTST: A top- k token selective transformer for remote sensing image super-resolution","volume":"33","author":"Xiao","year":"2024","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.compmedimag.2026.102820_bib32","doi-asserted-by":"crossref","first-page":"1783","DOI":"10.1109\/TMM.2024.3521798","article-title":"Frequency-assisted mamba for remote sensing image super-resolution","volume":"27","author":"Xiao","year":"2025","journal-title":"IEEE Trans. Multimed."},{"key":"10.1016\/j.compmedimag.2026.102820_bib33","doi-asserted-by":"crossref","DOI":"10.1016\/j.compmedimag.2024.102438","article-title":"An anthropomorphic diagnosis system of pulmonary nodules using weak annotation-based deep learning","volume":"118","author":"Xie","year":"2024","journal-title":"Comput. Med. Imaging Graph."},{"key":"10.1016\/j.compmedimag.2026.102820_bib34","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.110216","article-title":"Fault diagnosis of motor bearing in complex scenarios based on mamba and indicative contrastive learning","volume":"146","author":"Xu","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.compmedimag.2026.102820_bib35","article-title":"PC-DARTS: Partial channel connections for memory-efficient architecture search","author":"Xu","year":"2020","journal-title":"Presente Int. Conf. Learn. Represent."},{"key":"10.1016\/j.compmedimag.2026.102820_bib36","doi-asserted-by":"crossref","DOI":"10.1016\/j.compmedimag.2025.102511","article-title":"Prior knowledge-based multi-task learning network for pulmonary nodule classification","volume":"121","author":"Xue","year":"2025","journal-title":"Comput. Med. Imaging Graph."},{"key":"10.1016\/j.compmedimag.2026.102820_bib37","series-title":"Presented at the Asian Conference on Computer Vision","first-page":"91","article-title":"Classification of lung nodule malignancy risk on computed tomography images using convolutional neural network: a comparison between 2D and 3D strategies","author":"Yan","year":"2017"},{"key":"10.1016\/j.compmedimag.2026.102820_bib38","doi-asserted-by":"crossref","first-page":"843","DOI":"10.1007\/s11760-023-02807-5","article-title":"A 3D prediction model for benign or malignant of pulmonary nodules based on neural architecture search","volume":"18","author":"Yang","year":"2024","journal-title":"Signal. Image Video Process."},{"key":"10.1016\/j.compmedimag.2026.102820_bib39","doi-asserted-by":"crossref","DOI":"10.1016\/j.compmedimag.2025.102626","article-title":"TG-mamba: leveraging text guidance for predicting tumor mutation burden in lung cancer","volume":"124","author":"Yu","year":"2025","journal-title":"Comput. Med. Imaging Graph."},{"key":"10.1016\/j.compmedimag.2026.102820_bib40","series-title":"2025 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Presented at the 2025 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"4484","article-title":"MambaOut: Do we really need mamba for vision?","author":"Yu","year":"2025"},{"key":"10.1016\/j.compmedimag.2026.102820_bib41","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.110536","article-title":"Dual-branch crack segmentation network with multi-shape kernel based on convolutional neural network and mamba","volume":"150","author":"Zhang","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.compmedimag.2026.102820_bib42","article-title":"Attention-guided deep neural network with a multichannel architecture for lung nodule classification","volume":"10","author":"Zheng","year":"2024","journal-title":"Heliyon"},{"key":"10.1016\/j.compmedimag.2026.102820_bib43","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2025.111768","article-title":"GraphMamba: Whole slide image classification meets graph-driven selective state space model","volume":"167","author":"Zheng","year":"2025","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.compmedimag.2026.102820_bib44","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.130733","article-title":"GCNet-mamba: leveraging state space models and CNN for medical image classification","volume":"303","author":"Zhou","year":"2026","journal-title":"Expert. Syst. Appl."},{"key":"10.1016\/j.compmedimag.2026.102820_bib45","first-page":"62429","article-title":"Vision mamba: efficient visual representation learning with bidirectional state space model","volume":"235","author":"Zhu","year":"2024","journal-title":"Proc. Mach. Learn. Res."},{"key":"10.1016\/j.compmedimag.2026.102820_bib46","article-title":"Cross-ViT based benign and malignant classification of pulmonary nodules","volume":"20","author":"Zhu","year":"2025","journal-title":"PLOS ONE"},{"key":"10.1016\/j.compmedimag.2026.102820_bib47","series-title":"Presented at the 2018 IEEE Winter Conference on Applications of Computer Vision (WACV)","first-page":"673","article-title":"DeepLung: Deep 3D dual path nets for automated pulmonary nodule detection and classification","author":"Zhu","year":"2018"}],"container-title":["Computerized Medical Imaging and Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0895611126001230?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0895611126001230?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T14:47:22Z","timestamp":1791211642000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0895611126001230"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":47,"alternative-id":["S0895611126001230"],"URL":"https:\/\/doi.org\/10.1016\/j.compmedimag.2026.102820","relation":{},"ISSN":["0895-6111"],"issn-type":[{"value":"0895-6111","type":"print"}],"subject":[],"published":{"date-parts":[[2026,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"DARTS-searched feedback-augmented state space model for 3D pulmonary nodule classification","name":"articletitle","label":"Article Title"},{"value":"Computerized Medical Imaging and Graphics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compmedimag.2026.102820","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"102820"}}