乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,7,7]],"date-time":"2024-07-07T23:57:04Z","timestamp":1720396624130},"reference-count":50,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neurocomputing"],"published-print":{"date-parts":[[2023,9]]},"DOI":"10.1016\/j.neucom.2023.126464","type":"journal-article","created":{"date-parts":[[2023,6,15]],"date-time":"2023-06-15T19:42:55Z","timestamp":1686858175000},"page":"126464","update-policy":"http:\/\/dx.doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Deep learning-based covert brain infarct detection from multiple MRI sequences"],"prefix":"10.1016","volume":"550","author":[{"given":"Sicheng","family":"Zhao","sequence":"first","affiliation":[]},{"given":"Hamid F.","family":"Bagce","sequence":"additional","affiliation":[]},{"given":"Vadim","family":"Spektor","sequence":"additional","affiliation":[]},{"given":"Yen","family":"Chou","sequence":"additional","affiliation":[]},{"given":"Ge","family":"Gao","sequence":"additional","affiliation":[]},{"given":"Clarissa D.","family":"Morales","sequence":"additional","affiliation":[]},{"given":"Hao","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Jingchen","family":"Ma","sequence":"additional","affiliation":[]},{"given":"Lawrence H.","family":"Schwartz","sequence":"additional","affiliation":[]},{"given":"Jennifer J.","family":"Manly","sequence":"additional","affiliation":[]},{"given":"Richard P.","family":"Mayeux","sequence":"additional","affiliation":[]},{"given":"Adam M.","family":"Brickman","sequence":"additional","affiliation":[]},{"given":"Jose D.","family":"Gutierrez","sequence":"additional","affiliation":[]},{"given":"Binsheng","family":"Zhao","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.neucom.2023.126464_b0005","doi-asserted-by":"crossref","first-page":"art. 72","DOI":"10.3389\/fnagi.2013.00072","article-title":"Accumulation of MRI markers of cerebral small vessel disease is associated with decreased cognitive function. A study in first-ever stroke and hypertensive patients","volume":"5","author":"Huijts","year":"2013","journal-title":"Front. Aging Neurosci."},{"issue":"14","key":"10.1016\/j.neucom.2023.126464_b0010","doi-asserted-by":"crossref","first-page":"1230","DOI":"10.1212\/01.wnl.0000345666.83318.03","article-title":"Silent ischemic infarcts are associated with hemorrhage burden in cerebral amyloid angiopathy","volume":"72","author":"Kimberly","year":"2009","journal-title":"Neurology"},{"issue":"13","key":"10.1016\/j.neucom.2023.126464_b0015","doi-asserted-by":"crossref","first-page":"1215","DOI":"10.1056\/NEJMoa022066","article-title":"Silent brain infarcts and the risk of dementia and cognitive decline","volume":"348","author":"Vermeer","year":"2003","journal-title":"N. Engl. J. Med."},{"key":"10.1016\/j.neucom.2023.126464_b0020","doi-asserted-by":"crossref","unstructured":"M. P. Heinrich, M. Jenkinson, S. M. Brady, J. A. Schnabel, Globally optimal deformable registration on a minimum spanning tree using dense displacement sampling, in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2012, pp. 115\u2013122.","DOI":"10.1007\/978-3-642-33454-2_15"},{"issue":"7","key":"10.1016\/j.neucom.2023.126464_b0025","doi-asserted-by":"crossref","first-page":"1239","DOI":"10.1109\/TMI.2013.2246577","article-title":"Mrf-based deformable registration and ventilation estimation of lung ct","volume":"32","author":"Heinrich","year":"2013","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.neucom.2023.126464_b0030","doi-asserted-by":"crossref","unstructured":"Z. Tian, C. Shen, H. Chen, and T. He, Fcos: Fully convolutional one-stage object detection, in IEEE\/CVF International Conference on Computer Vision, 2019, pp. 9627-9636.","DOI":"10.1109\/ICCV.2019.00972"},{"issue":"11","key":"10.1016\/j.neucom.2023.126464_b0035","doi-asserted-by":"crossref","first-page":"3212","DOI":"10.1109\/TNNLS.2018.2876865","article-title":"Object detection with deep learning: A review","volume":"30","author":"Zhao","year":"2019","journal-title":"IEEE Trans. Neural Networks Learn. Syst."},{"issue":"2","key":"10.1016\/j.neucom.2023.126464_b0040","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1007\/s11263-019-01247-4","article-title":"Deep learning for generic object detection: a survey","volume":"128","author":"Liu","year":"2020","journal-title":"Int. J. Comput. Vis."},{"key":"10.1016\/j.neucom.2023.126464_b0045","doi-asserted-by":"crossref","DOI":"10.1016\/j.dsp.2022.103514","article-title":"A survey of modern deep learning based object detection models","author":"Zaidi","year":"2022","journal-title":"Digital Signal Process."},{"key":"10.1016\/j.neucom.2023.126464_b0050","doi-asserted-by":"crossref","unstructured":"R. Girshick, J. Donahue, T. Darrell, J. Malik, Rich feature hierarchies for accurate object detection and semantic segmentation, in IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 580-587.","DOI":"10.1109\/CVPR.2014.81"},{"key":"10.1016\/j.neucom.2023.126464_b0055","doi-asserted-by":"crossref","unstructured":"R. Girshick, \u201cFast r-cnn,\u201d in IEEE International Conference on Computer Vision, 2015, pp. 1440-1448.","DOI":"10.1109\/ICCV.2015.169"},{"key":"10.1016\/j.neucom.2023.126464_b0060","unstructured":"S. Ren, K. He, R. Girshick, J. Sun, Faster r-cnn: Towards real-time object detection with region proposal networks, Adv. Neural Inf. Process. Syst., vol. 28, 2015."},{"key":"10.1016\/j.neucom.2023.126464_b0065","doi-asserted-by":"crossref","unstructured":"K. He, G. Gkioxari, P. Dol\u013aar, and R. Girshick, \u201cMask r-cnn,\u201d in IEEE International Conference on Computer Vision, 2017, pp. 2961\u20132969.","DOI":"10.1109\/ICCV.2017.322"},{"key":"10.1016\/j.neucom.2023.126464_b0070","doi-asserted-by":"crossref","unstructured":"Z. Cai, N. Vasconcelos, \u201cCascade r-cnn: Delving into high quality object detection,\u201d in IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 6154-6162.","DOI":"10.1109\/CVPR.2018.00644"},{"key":"10.1016\/j.neucom.2023.126464_b0075","doi-asserted-by":"crossref","unstructured":"J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, You only look once: Unified, real-time object detection, in: IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 779-788.","DOI":"10.1109\/CVPR.2016.91"},{"key":"10.1016\/j.neucom.2023.126464_b0080","doi-asserted-by":"crossref","unstructured":"J. Redmon and A. Farhadi, Yolo9000: better, faster, stronger, in: IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 7263-7271.","DOI":"10.1109\/CVPR.2017.690"},{"key":"10.1016\/j.neucom.2023.126464_b0085","unstructured":"J. Redmon and A. Farhadi, Yolov3: An incremental improvement, arXiv preprint arXiv:1804.02767, 2018."},{"key":"10.1016\/j.neucom.2023.126464_b0090","unstructured":"A. Bochkovskiy, C.-Y. Wang, and H.-Y. M. Liao, Yolov4: Optimal speed and accuracy of object detection, arXiv preprint arXiv:2004.10934, 2020."},{"key":"10.1016\/j.neucom.2023.126464_b0095","doi-asserted-by":"crossref","unstructured":"W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg, Ssd: Single shot multibox detector, in European Conference on Computer Vision, 2016, pp. 21\u201337.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"10.1016\/j.neucom.2023.126464_b0100","doi-asserted-by":"crossref","unstructured":"T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dol\u013aar, Focal loss for dense object detection, in IEEE International Conference on Computer Vision, 2017, pp. 2980\u20132988.","DOI":"10.1109\/ICCV.2017.324"},{"key":"10.1016\/j.neucom.2023.126464_b0105","unstructured":"X. Li, W. Wang, L. Wu, S. Chen, X. Hu, J. Li, J. Tang, and J. Yang, Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection, Adv. Neural Inf. Process. Syst., vol. 33, 21 002\u201321 012, 2020."},{"key":"10.1016\/j.neucom.2023.126464_b0110","doi-asserted-by":"crossref","unstructured":"M. Tan, R. Pang, and Q. V. Le, Efficientdet: Scalable and efficient object detection, in IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 10 781-10 790.","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"10.1016\/j.neucom.2023.126464_b0115","doi-asserted-by":"crossref","unstructured":"V. Alex, M. S. KP, S. S. Chennamsetty, and G. Krishnamurthi, Generative adversarial networks for brain lesion detection, in Medical Imaging 2017: Image Processing, vol. 10133, 2017, p. 101330G.","DOI":"10.1117\/12.2254487"},{"key":"10.1016\/j.neucom.2023.126464_b0120","doi-asserted-by":"crossref","first-page":"526","DOI":"10.1016\/j.neucom.2016.09.051","article-title":"Detection of brain tumor in 3D MRI images using local binary patterns and histogram orientation gradient","volume":"219","author":"Abbasi","year":"2017","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2023.126464_b0125","unstructured":"X. Chen, E. Konukoglu, Unsupervised detection of lesions in brain mri using constrained adversarial auto-encoders, in International Conference on Medical Imaging with Deep Learning, 2018."},{"key":"10.1016\/j.neucom.2023.126464_b0130","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1016\/j.neucom.2017.12.032","article-title":"Segmentation of glioma tumors in brain using deep convolutional neural network","volume":"282","author":"Hussain","year":"2018","journal-title":"Neurocomputing"},{"issue":"8","key":"10.1016\/j.neucom.2023.126464_b0135","doi-asserted-by":"crossref","first-page":"2303","DOI":"10.1109\/JBHI.2020.2964016","article-title":"An adversarial learning approach to medical image synthesis for lesion detection","volume":"24","author":"Sun","year":"2020","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.neucom.2023.126464_b0140","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1016\/j.neucom.2019.01.111","article-title":"Brain tumor segmentation with deep convolutional symmetric neural network","volume":"392","author":"Chen","year":"2020","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2023.126464_b0145","doi-asserted-by":"crossref","first-page":"643","DOI":"10.1016\/j.nicl.2017.09.003","article-title":"Brain lesion segmentation through image synthesis and outlier detection","volume":"16","author":"Bowles","year":"2017","journal-title":"NeuroImage: Clinical"},{"key":"10.1016\/j.neucom.2023.126464_b0150","doi-asserted-by":"crossref","unstructured":"P. Moeskops, J. de Bresser, H. J. Kuijf, A. M. Mendrik, G. J. Biessels, J. P. Pluim, and I. I\u02c7sgum, Evaluation of a deep learning approach for the segmentation of brain tissues and white matter hyperintensities of presumed vascular origin in mri, NeuroImage: Clinical, vol. 17, pp. 251-262, 2018.","DOI":"10.1016\/j.nicl.2017.10.007"},{"key":"10.1016\/j.neucom.2023.126464_b0155","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/j.neucom.2019.12.050","article-title":"Deep convolutional neural network for accurate segmentation and quantification of white matter hyperintensities","volume":"384","author":"Liu","year":"2020","journal-title":"Neurocomputing"},{"issue":"4","key":"10.1016\/j.neucom.2023.126464_b0160","doi-asserted-by":"crossref","first-page":"727","DOI":"10.1007\/s00234-021-02820-w","article-title":"A deep learning algorithm for white matter hyperintensity lesion detection and segmentation","volume":"64","author":"Zhang","year":"2022","journal-title":"Neuroradiology"},{"key":"10.1016\/j.neucom.2023.126464_b0165","doi-asserted-by":"crossref","unstructured":"C. Baur, B. Wiestler, S. Albarqouni, N. Navab, Deep autoencoding models for unsupervised anomaly segmentation in brain mr images, in International MICCAI Brainlesion Workshop, 2018, pp. 161-169.","DOI":"10.1007\/978-3-030-11723-8_16"},{"key":"10.1016\/j.neucom.2023.126464_b0170","doi-asserted-by":"crossref","unstructured":"C. Baur, R. Graf, B. Wiestler, S. Albarqouni, and N. Navab, Steganomaly: inhibiting cyclegan steganography for unsupervised anomaly detection in brain mri, in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2020, pp. 718-727.","DOI":"10.1007\/978-3-030-59713-9_69"},{"issue":"1","key":"10.1016\/j.neucom.2023.126464_b0175","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-021-87013-4","article-title":"An anomaly detection approach to identify chronic brain infarcts on mri","volume":"11","author":"van Hespen","year":"2021","journal-title":"Sci. Rep."},{"key":"10.1016\/j.neucom.2023.126464_b0180","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1016\/j.neucom.2022.11.041","article-title":"A spatiotemporal correlation deep learning network for brain penumbra disease","volume":"520","author":"Liu","year":"2023","journal-title":"Neurocomputing"},{"issue":"3","key":"10.1016\/j.neucom.2023.126464_b0185","doi-asserted-by":"crossref","first-page":"594","DOI":"10.1109\/TMI.2022.3213372","article-title":"Semi-supervised unpaired medical image segmentation through task-affinity consistency","volume":"42","author":"Chen","year":"2023","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.neucom.2023.126464_b0190","doi-asserted-by":"crossref","unstructured":"S. Akcay, A. Atapour-Abarghouei, and T.P., Breckon, Ganomaly: Semi-supervised anomaly detection via adversarial training, in Asian Conference on Computer Vision, 2018, pp. 622-637.","DOI":"10.1007\/978-3-030-20893-6_39"},{"issue":"4","key":"10.1016\/j.neucom.2023.126464_b0195","doi-asserted-by":"crossref","first-page":"406","DOI":"10.18383\/j.tom.2016.00223","article-title":"A response assessment platform for development and validation of imaging biomarkers in oncology","volume":"2","author":"Yang","year":"2016","journal-title":"Tomography"},{"issue":"4","key":"10.1016\/j.neucom.2023.126464_b0200","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1002\/ana.21326","article-title":"Frequency and course of mild cognitive impairment in a multiethnic community","volume":"63","author":"Manly","year":"2008","journal-title":"Ann. Neurol."},{"issue":"1","key":"10.1016\/j.neucom.2023.126464_b0205","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1001\/jamaneurol.2022.3919","article-title":"Brain aging among racially and ethnically diverse middle-aged and older adults","volume":"80","author":"Turney","year":"2023","journal-title":"JAMA Neurology"},{"issue":"8","key":"10.1016\/j.neucom.2023.126464_b0210","doi-asserted-by":"crossref","first-page":"1788","DOI":"10.1109\/TMI.2019.2897538","article-title":"Voxelmorph: a learning framework for deformable medical image registration","volume":"38","author":"Balakrishnan","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"2","key":"10.1016\/j.neucom.2023.126464_b0215","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1109\/TPAMI.2018.2858826","article-title":"Focal loss for dense object detection","volume":"42","author":"Lin","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.neucom.2023.126464_b0220","doi-asserted-by":"crossref","unstructured":"J. Yu, Y. Jiang, Z. Wang, Z. Cao, T. Huang, Unitbox: An advanced object detection network, in ACM International Conference on Multimedia, 2016, pp. 516-520.","DOI":"10.1145\/2964284.2967274"},{"key":"10.1016\/j.neucom.2023.126464_b0225","doi-asserted-by":"crossref","unstructured":"S. Zhao, Y. Ma, Y. Gu, J. Yang, T. Xing, P. Xu, R. Hu, H. Chai, K. Keutzer, An End-to-End Visual-Audio Attention Network for Emotion Recognition in User-Generated Videos, in AAAI Conference on Artificial Intelligence, 2020, pp. 303-311.","DOI":"10.1609\/aaai.v34i01.5364"},{"key":"10.1016\/j.neucom.2023.126464_b0230","doi-asserted-by":"crossref","unstructured":"K. He, X. Zhang, S. Ren, and J. Sun, Deep residual learning for image recognition, in IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 770-778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"10.1016\/j.neucom.2023.126464_b0235","doi-asserted-by":"crossref","unstructured":"X. Wang, Y. Peng, L. Lu, Z. Lu, M. Bagheri, and R. M. Summers, Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases, in IEEE conference on Computer Vision and Pattern Pecognition, 2017, pp. 2097-2106.","DOI":"10.1109\/CVPR.2017.369"},{"key":"10.1016\/j.neucom.2023.126464_b0240","unstructured":"A. E. Johnson, T. J. Pollard, N. R. Greenbaum, M. P. Lungren, C. Y. Deng, Y. Peng, Z. Lu, R. G. Mark, S. J. Berkowitz, and S. Horng, MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs, arXiv preprint arXiv:1901.07042."},{"key":"10.1016\/j.neucom.2023.126464_b0245","doi-asserted-by":"crossref","unstructured":"J. Irvin, P. Rajpurkar, M. Ko, Y. Yu, S. Ciurea-Ilcus, C. Chute, H. Marklund, B. Haghgoo, R. L. Ball, K. S. Shpanskaya, J. Seekins, D. A. Mong, S. S. Halabi, J. K. Sandberg, R. Jones, D. B. Larson, C. P. Langlotz, B. N. Patel, M. P. Lungren, A. Y. Ng, CheXpert: A large chest radiograph dataset with uncertainty labels and expert comparison, in AAAI Conference on Artificial Intelligence, 2019, pp. 590-597.","DOI":"10.1609\/aaai.v33i01.3301590"},{"key":"10.1016\/j.neucom.2023.126464_b0250","unstructured":"P. Rajpurkar, J. Irvin, A. Bagul, D. Y. Ding, T. Duan, H. Mehta, B. Yang, K. Zhu, D. Laird, R. L. Ball, C. P. Langlotz, K. S. Shpanskaya, M. P. Lungren, and A. Y. Ng, MURA Dataset: Towards Radiologist-Level Abnormality Detection in Musculoskeletal Radiographs, in: International Conference on Medical Imaging with Deep Learning, 2018."}],"container-title":["Neurocomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231223005878?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231223005878?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2023,8,19]],"date-time":"2023-08-19T04:06:34Z","timestamp":1692417994000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0925231223005878"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9]]},"references-count":50,"alternative-id":["S0925231223005878"],"URL":"https:\/\/doi.org\/10.1016\/j.neucom.2023.126464","relation":{},"ISSN":["0925-2312"],"issn-type":[{"value":"0925-2312","type":"print"}],"subject":[],"published":{"date-parts":[[2023,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Deep learning-based covert brain infarct detection from multiple MRI sequences","name":"articletitle","label":"Article Title"},{"value":"Neurocomputing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neucom.2023.126464","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2023 Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"126464"}}