浏览全部资源
扫码关注微信
纸质出版日期： 2020-10-16 ，
录用日期： 2020-07-17
移动端阅览
徐春园, 曾晓天, 宋泽雨, 唐晓英. 4D时空纵向分析在生物医学领域中的应用现状与趋势[J]. 中国图象图形学报, 2020,25(10):2100-2109.
Chunyuan Xu, Xiaotian Zeng, Zeyu Song, Xiaoying Tang. Research status and trend of 4D spatiotemporal longitudinal analysis in biomedical field[J]. Journal of Image and Graphics, 2020,25(10):2100-2109.
随着成像技术的成熟，临床医生及实验人员能获得同时带有时间和空间信息的4D（3D+时间）数据，用于纵向研究疾病变化情况。由于缺乏合适的处理算法，导致明显的信息丢失。为解决这一问题，一些研究发挥人工智能在海量数据处理上的优势进行纵向医学图像分析，研究目标随时间的动态变化。本文对4D时空纵向分析在生物学运动目标追踪、医学影像分割、肿瘤生长预测、血管动力学和神经科学等应用进行综述，重点探讨了人工智能技术与传统分析方法在各应用场景的优劣，并从联合多模态异构数据进行关联分析及联邦学习辅助算法部署两个角度进行前瞻性的探索和可行性分析，突破2D影像处理瓶颈，推动4D设备广泛应用，并为未来时空纵向分析在生物医学领域中的方法学研究及应用场景探索提供思路。
Clinicians and experimenters can obtain a time series of three-dimensional images in a fixed time period
that is
4D (3D + time) longitudinal data with both time and space information
such as two-photon microscopy
CT/MRI(computer tomography/magnetic resonance imaging) Cine scanning mode
or artificially select a time point to scan
and use 4D tensor representation to integrate the collected data in one time and three spatial dimensions
which is suitable for longitudinal analysis. Longitudinal analysis describes the collection of data on one or more variables of the same object at multiple time points to study their changes over time or a set of diachronic research methods that track the influence of some variables
which are commonly used in the medical field: disease changes and causes. However
many studies in the past sliced 4D data into 2D/3D pictures due to the lack of suitable processing algorithms
resulting in significant information loss. In recent years
artificial intelligence has given full play to its natural advantages in massive data processing and has brought new solutions to solve the problems of 4D longitudinal data with high dimensions
large amount of calculation
and difficulty in analysis. Among the solutions
convolutional neural networks
long- and short-term memory networks
and other deep learning algorithms have achieved good results in the processing of different modalities
such as natural language
audio
image
and video. They have exceeded the traditional methods. In the longitudinal medical image analysis using 4D data
deep learning uses spatial information and time-varying information and plays an important role in studying the dynamic changes of goals over time. The main application directions can be divided into two categories: moving target tracking and positioning in the field of biomedicine and tumor growth prediction and auxiliary diagnosis
where moving target tracking and positioning includes matching between complex moving targets in the biological field
4D longitudinal medical imaging
automatic data segmentation
and vascular dynamics research. Tumor growth prediction and auxiliary diagnosis use volume longitudinal data with time information to model tumor growth
calculate the change in tumor size over a period of time
that is
the growth characteristics of the tumor
and assist the doctor in the diagnostic stage from the perspective of growth rate (benign tumors generally grow slower than malignant tumors). Cancer grades are recommended for patients to review. During the treatment phase
tumor changes are accurately measured; the effects of radiotherapy or drug treatment are evaluated; survival is predicted; personalized treatment plans for patients are developed
and drug development is promoted. However
the abovementioned longitudinal analysis only focuses on the change of the macroscopic morphology of the lesion and cannot reflect all tumor biological information or predict the clinically relevant tumor properties. In the future
horizontal correlation analysis can include temporal features
capture the relationship between temporal and spatial changes
and map the quantitative values of molecular features to histopathological changes
thereby proving the relationship between the secretion of cytokines and the severity of lesions in the image relevance to explain the causality of the disease from the table and achieve personalized treatment. In addition
the accuracy and generalization of the abovementioned artificial intelligence algorithms depend on large-scale and high-quality data. Medical data have fewer samples
larger acquisition costs
and higher resolution requirements than data in other fields. Problem and longitudinal data make data collection difficult because they contain time information not previously involved. Future medical longitudinal data points can be combined with federal learning and transfer learning: federal learning is used to ensure that no data exchange is observed while improving the generalization of the model
making the same model suitable for data from other hospitals
and protecting the privacy of medical data. On the contrary
transfer learning is used to solve the problem of few high-quality samples and the lack of frame-by-frame labels
improve the accuracy of the model
and solve hidden security risks such as user privacy when the product is launched in the future.
人工智能医学影像处理纵向分析4D影像时空数据
artificial intelligencemedical image processinglongitudinal analysis4D imagespatiotemporal data
Baltrusaitis T, Ahuja C and Morency L P. 2019. Multimodal machine learning:a survey and taxonomy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(1):423-443[DOI:10.1109/tpami.2018.2798607]
Bengs M, Gessert N and Schlaefer A. 2020.4D spatio-temporal deep learning with 4D fMRI data for autism spectrum disorder classification.[EB/OL].[2020-05-01].https://arxiv.org/pdf/2004.10165v1.pdfhttps://arxiv.org/pdf/2004.10165v1.pdf
Chang K, Balachandar N, Lam C, Yi D, Brown J, Beers A, Rosen B, Rubin D L and Kalpathy-Cramer J. 2018. Distributed deep learning networks among institutions for medical imaging. Journal of the American Medical Informatics Association, 25(8):945-954[DOI:10.1093/jamia/ocy017]
Chen G T Y, Kung J H and Beaudette K P. 2004. Artifacts in computed tomography scanning of moving objects. Seminars in Radiation Oncology, 14(1):19-26[DOI:10.1053/j.semradonc.2003.10.004]
Deist T M, Jochems A, van Soest J, Nalbantov G, Oberije C, Walsh S, Eble M, Bulens P, Coucke P, Dries W, Dekker A and Lambin P. 2017. Infrastructure and distributed learning methodology for privacy-preserving multi-centric rapid learning health care:euroCAT. Clinical and Translational Radiation Oncology, 4:24-31[DOI:10.1016/j.ctro.2016.12.004]
Dennis E L, Babikian T, Giza C C, Thompson P M and Asarnow R F. 2018. Neuroimaging of the injured pediatric brain:methods and new lessons. The Neuroscientist, 24(6):652-670[DOI:10.1177/1073858418759489]
Dewhurst P, Coats L, Parikh J D, Hollingsworth K G and Gan L. 2020. The role of flow rotation in the adult right atrium:a 4D flow cardiovascular magnetic resonance study. Physiological Measurement, 41(3):#035007[DOI:10.1088/1361-6579/ab7 d77]
Eklund A, Nichols T E and Knutsson H. 2016. Cluster failure:why fMRI inferences for spatial extent have inflated false-positive rates. Proceedings of the National Academy of Sciences of the United States of America, 113(28):7900-7905[DOI:10.1073/pnas.1602413113]
El-Gazzar A, Quaak M, Cerliani L, Bloem P, van Wingen G and Thomas R M. 2020. A hybrid 3DCNN and 3DC-LSTM based model for 4D spatio-temporal fMRI data: an Abide autism classification study[EB/OL].[2020-05-01].https://arxiv.org/pdf/2002.05981.pdfhttps://arxiv.org/pdf/2002.05981.pdf[DOI:10.1007/978-3-030-32695-1_11http://dx.doi.org/10.1007/978-3-030-32695-1_11].
Feng R, Wang Z Q, Zhu H X and Song Y L. 2019. The values of 4 d-CTA combined with CTP technology in preoperative evaluation of meningiomas adjacent to large blood vessels. Chinese Journal of CT and MRI, 17(1):37-40
冯瑞, 王志群, 祝红线, 宋云龙. 2019.4D-CTA联合CTP在紧邻大血管脑膜瘤术前评估中的价值.中国CT和MRI杂志, 17(1):37-40 [DOI:10.3969/j.issn.1672-5131.2019.01.012]
Feng Y H, Li J, Gao R X and Pang X. 2020. Application value of 4D-CT angiography in pulmonary artery imaging of patients with acute pulmonary embolism. Medical Equipment, 33(4):8-9
冯永恒, 李军, 高瑞雪, 庞鑫. 2020.4D-CT血管成像技术在急性肺栓塞患者肺动脉成像中的应用价值.医疗装备, 33(4):8-9 [DOI:10.3969/j.issn.1002-2376.2020.04.005]
Fried D V, Tucker S L, Zhou S H, LiaoZ X, Mawlawi O, Ibbott G and Court L E. 2014. Prognostic value and reproducibility of pretreatment CT texture features in stage Ⅲ non-small cell lung cancer. International Journal of Radiation Oncology Biology Physics, 90(4):834-842[DOI:10.1016/j.ijrobp.2014.07.020]
Gao Y, Phillips J M, Zheng Y, Min R Q, Fletcher P T and Gerig G. 2018. Fully convolutional structured LSTM networks for joint 4D medical image segmentation//Proceedings of the 15th IEEE International Symposium on Biomedical Imaging. Washington: IEEE: 1104-1108[DOI:10.1109/ISBI.2018.8363764http://dx.doi.org/10.1109/ISBI.2018.8363764]
Gao Y, Zhang M M, Grewen K, Fletcher P T and Gerig G. 2016. Image registration and segmentation in longitudinal MRI using temporal appearance modeling//Proceedings of the 13th IEEE International Symposium on Biomedical Imaging. Prague, Czech Republic: IEEE: 629-632[DOI:10.1109/ISBI.2016.7493346http://dx.doi.org/10.1109/ISBI.2016.7493346]
Gooya A, Pohl K M, Bilello M, Cirillo L, Biros G, Melhem E R and Davatzikos C. 2012. GLISTR:glioma image segmentation and registration. IEEE Transactions on Medical Imaging, 31(10):1941-1954[DOI:10.1109/tmi.2012.2210558]
Gur S, Wolf L, Golgher L and Blinder P. 2020. Microvascular dynamics from 4D microscopy using temporal segmentation. Pacific Symposium on Biocomputing, 25:331-342[DOI:10.1142/9789811215636_0030]
Gutman D A, Cooper L A D, Hwang S N, Holder C A, Gao J J, Aurora T D, Dunn W D Jr, Scarpace L, Mikkelsen T, Jain R, Wintermark M, Jilwan M, Raghavan P, Huang E, Clifford R J, Mongkolwat P, Kleper V, Freymann J, Kirby J, Zinn P O, Moreno C S, Jaffe C, Colen R, Rubin D L, Saltz J, Flanders A and Brat D J. 2013. MR imaging predictors of molecular profile and survival:multi-institutional study of the TCGA glioblastoma data set. Radiology, 267(2):560-569[DOI:10.1148/radiol.13120118]
He T, Mao H, Guo J X and Yi Z. 2017. Cell tracking using deep neural networks with multi-task learning. Image and Vision Computing, 60:142-153[DOI:10.1016/j.imavis.2016.11.010]
Henker C, Kriesen T, Glass Ä, Schneider B and Piek J. 2017. Volumetric quantification of glioblastoma:experiences with different measurement techniques and impact on survival. Journal of Neuro-Oncology, 135(2):391-402[DOI:10.1007/s11060-017-2587-5]
Hossain M S and Muhammad G. 2019. An audio-visual emotion recognition system using deep learning fusion for a cognitive wireless framework. IEEE Wireless Communications, 26(3):62-68[DOI:10.1109/mwc.2019.1800419]
Hou Y, Konen J, Brat D J, Marcus A I and Cooper L A D. 2018. TASI:a software tool for spatial-temporal quantification of tumor spheroid dynamics. Scientific Reports, 8:#7248[DOI:10.1038/s41598-018-25337-4]
Huang H, Zhang M, Chen C, Zhang H L, Wei Y Q, Tian J B, Shang J, Deng Y, Du A H and Dai H P. 2020. Clinical Characteristics of COVID-19 in patients with pre-existing ILD: a retrospective study in a single center in Wuhan, China.[EB/OL].[2020-05-01].https://onlinelibrary.wiley.com/doi/pdf/10.1002/jmv.26174https://onlinelibrary.wiley.com/doi/pdf/10.1002/jmv.26174[DOI:10.1002/jmv.26174http://dx.doi.org/10.1002/jmv.26174].
In't Veld M, Fronczek R, dos Santos M P, van Walderveen M A A, Meijer F J A, Willems P W A and Zhang X Q. 2020. High sensitivity and specificity of 4D-CTA in detecting intracranial arteriovenous shunt. International Journal of Medical Radiology, 43(1):#119
In't Veld M, Fronczek R, dos Santos M P, van Walderveen M A A, Meijer F J A, Willems P W A, 张晓琦. 2020.4 d-cta在检测颅内动静脉分流中的高敏感度和特异度.国际医学放射学杂志, 43(1):#119 [DOI:10.19300/j.2020.e1107]
Janas K, Kudla M J, Janas A, Blicharz-Dorniak J and Kos-Kudla B. 2019. Spatio-temporal image correlation (STIC) in evaluation of advanced neuroendocrine tumours. Endokrynologia Polska, 70(3):219-223[DOI:10.5603/EP.a2019.0001]
Jiang X X, Luo G H, Zhou F and Liu W H. 2020. Application value of 4D-CT angiography in the diagnosis of intracranial aneurysm. Journal of China Medical University, 49(4):336-341
蒋孝先, 罗光华, 周飞, 刘文洪. 2020.4D-CTA在颅内动脉瘤诊断中的应用价值.中国医科大学学报, 49(4):336-341 [DOI:10.12007/j.issn.0258-4646.2020.04.010]
Jiao Y, Weng M and Yang M. 2019. Multi-object portion tracking in 4D fluorescence microscopy imagery with deep feature maps//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Long Beach: IEEE: 1087-1096[DOI:10.1109/cvprw.2019.00142http://dx.doi.org/10.1109/cvprw.2019.00142]
Kickingereder P, Isensee F, Tursunova I, Petersen J, Neuberger U, Bonekamp D, Brugnara G, Schell M, Kessler T, Foltyn M, Harting I, Sahm F, Prager M, Nowosielski M, Wick A, Nolden M, Radbruch A, Debus J, Schlemmer H P, Heiland S, Platten M, von Deimling A, van den Bent M J, Gorlia T, Wick W, Bendszus M and Maier-Hein K H. 2019. Automated quantitative tumour response assessment of MRI in neuro-oncology with artificial neural networks:a multicentre, retrospective study. The Lancet Oncology, 20(5):728-740[DOI:10.1016/s1470-2045(19)30098-1]
Kitrungrotsakul T, Han X H, Iwamoto Y, Takemoto S, Yokota H, Ipponjima S, Nemoto T, Wei X and Chen Y W. 2019. A cascade of 2.5D CNN and bidirectional CLSTM network for mitotic cell detection in 4D microscopy image. IEEE/ACM Transactions on Computational Biology and Bioinformatics[DOI:10.1109/tcbb.2019.2919015http://dx.doi.org/10.1109/tcbb.2019.2919015]
Li H W. 2019. Value of CTP and 4D-CTA in evaluating cerebrovascular stenosis or occlusion. Chinese Journal of CT and MRI, 17(3):69-71, 112
李红伟. 2019. CTP与4D-CTA在评估脑血管狭窄或闭塞的价值研究.中国CT和MRI杂志, 17(3):69-71, 112 [DOI:10.3969/j.issn.1672-5131.2019.03.022]
Li Q, Dong Q L, Ge F F, Qiang N, Zhao Y, Wang H, Huang H, Wu X and Liu T M. 2019. Simultaneous spatial-temporal decomposition of connectome-scale brain networks by deep sparse recurrent auto-encoders//Proceedings of the 26th International Conference on Information Processing in Medical Imaging. Hong Kong, China: Springer: 579-591[DOI:10.1007/978-3-030-20351-1_45http://dx.doi.org/10.1007/978-3-030-20351-1_45]
Li W, Lin X F and Chen X. 2020. Detecting Alzheimer's disease based on 4D fMRI:an exploration under deep learning framework. Neurocomputing, 388:280-287[DOI:10.1016/j.neucom.2020.01.053]
Li X D, Deng Z H, Deng Q H, Zhang L D, Niu T Y and Kuang Y. 2018. A novel deep learning framework for internal gross target volume definition from 4D computed tomography of lung cancer patients. IEEE Access, 6:37775-37783[DOI:10.1109/access.2018.2851027]
Liu Y X, Sadowski S M, Weisbrod A B, Kebebew E, Summers R M and Yao J H. 2014. Patient specific tumor growth prediction using multimodal images. Medical Image Analysis, 18(3):555-566[DOI:10.1016/j.media.2014.02.005]
Lou Y F, Irimia A, Vela P A, Chambers M C, van Horn J D, Vespa P M and Tannenbaum A R. 2013. Multimodal deformable registration of traumatic brain injury MR volumes via the Bhattacharyya distance. IEEE Transactions on Biomedical Engineering, 60(9):2511-2520[DOI:10.1109/tbme.2013.2259625]
Ma J X. 2020. Longitudinal study:mearsuring changes and explaining the causal relationship. Document, Information and Knowledge, (1):31-35
马晶鑫. 2020.纵向研究——测量变化和解释因果关系.图书情报知识, (1):31-35 [DOI:10.13366/j.dik.2010.01.018]
Martins W P, Welsh A W, Lima J C, Nastri C O and Raine-Fenning N J. 2011. The "volumetric" pulsatility index as evaluated by spatiotemporal imaging correlation (STIC):a preliminary description of a novel technique, its application to the endometrium and an evaluation of its reproducibility. Ultrasound in Medicine and Biology, 37(12):2160-2168[DOI:10.1016/j.ultrasmedbio.2011.08.014]
Menze B H, van Leemput K, Honkela A, Konukoglu E, Weber M A, Ayache N and Golland P. 2011. A generative approach for image-based modeling of tumor growth//Proceedings of the 22nd Information Processing in Medical Imaging. Kloster Irsee:Springer: 735-747[DOI:10.1007/978-3-642-22092-0_60http://dx.doi.org/10.1007/978-3-642-22092-0_60]
Müller M, Bibi A, Giancola S, Alsubaihi S and Ghanem B. 2018. TrackingNet: a large-scale dataset and benchmark for object tracking in the wild//Proceedings of the 15th European Conference on Computer Vision. Munich: Springer: #434[DOI:10.1007/978-3-030-01246-5_19http://dx.doi.org/10.1007/978-3-030-01246-5_19]
Nakamura T, Matsumine A, Matsubara T, Asanuma K, Uchida A and Sudo A. 2011. Clinical impact of the tumor volume doubling time on sarcoma patients with lung metastases. Clinical and Experimental Metastasis, 28(8):819-825[DOI:10.1007/s10585-011-9413-9]
Nath S K, Sandhu A P, Jensen L, Kim D, Bharne A, Nobiensky P D, Lawson J D, Fuster M M, Bazhenova L, Song W Y and Mundt A J. 2011. Frameless image-guided stereotactic body radiation therapy for lung tumors with 4-dimensional computed tomography or 4-dimensional positron emission tomography/computed tomography. Clinical Lung Cancer, 12(3):180-186[DOI:10.1016/j.cllc.2011.03.014]
Ou Y M, Sotiras A, Paragios N and Davatzikos C. 2011. Dramms:deformable registration via attribute matching and mutual-saliency weighting. Medical Image Analysis, 15(4):622-639[DOI:10.1016/j.media.2010.07.002]
Petridis S, Stafylakis T, Ma P E H, Cai F P, Tzimiropoulos G and Pantic M. 2018. End-to-end audiovisual speech recognition//Proceedings of 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing. Calgary: IEEE: 6548-6552[DOI:10.1109/ICASSP.2018.8461326http://dx.doi.org/10.1109/ICASSP.2018.8461326]
Pham J, Harris W, Sun W Z, Yang Z, Yin F F and Ren L. 2019. Predicting real-time 3D deformation field maps (DFM) based on volumetric cine MRI (VC-MRI) and artificial neural networks for on board 4D target tracking:a feasibility study. Physics in Medicine and Biology, 64(16):#165016[DOI:10.1088/1361-6560/ab359a]
Puranik M, Shah H, Shah K and Bagul S. 2018. Intelligent alzheimer's detector using deep learning//Proceedings of the 2nd International Conference on Intelligent Computing and Control Systems. Madurai: IEEE: 318-323[DOI:10.1109/ICCONS.2018.8663065http://dx.doi.org/10.1109/ICCONS.2018.8663065]
Qi S L, Sui J, Chen J Y, Liu J Y, Jiang R T, Silva R, Iraji A, Damaraju E, Salman M, Lin D D, Fu Z N, Zhi D M, Turner J A, Bustillo J, Ford J M, Mathalon D H, Voyvodic J, McEwen S, Preda A, Belger A, Potkin S G, Mueller B A, Adali T and Calhoun V D. 2019. Parallel group ICA + ICA:joint estimation of linked functional network variability and structural covariation with application to schizophrenia. Human Brain Mapping, 40(13):3795-3809[DOI:10.1002/hbm.24632]
Qi S L, Yang X, Zhao L S, Calhoun V D, Perrone-Bizzozero N, Liu S F, Jiang R T, Jiang T Z, Sui J and Ma X H. 2018. MicroRNA132 associated multimodal neuroimaging patterns in unmedicated major depressive disorder. Brain, 141(3):916-926[DOI:10.1093/brain/awx366]
Reuter M, Schmansky N J, Rosas H D and Fischl B. 2012. Within-subject template estimation for unbiased longitudinal image analysis. Neuroimage, 61(4):1402-1418[DOI:10.1016/j.neuroimage.2012.02.084]
Roque T, Risser L, Kersemans V, Smart S, Allen D, Kinchesh P, Gilchrist S, Gomes A L, Schnabel J A and Chappell M A. 2018. A DCE-MRI driven 3-D reaction-diffusion model of solid tumor growth. IEEE Transactions on Medical Imaging, 37(3):724-732[DOI:10.1109/tmi.2017.2779811]
Sarraf S, Desouza D D, Anderson J A E and Saverino C. 2019. MCADNNet:recognizing stages of cognitive impairment through efficient convolutional fMRI and MRI neural network topology models. IEEE Access, 7:155584-155600[DOI:10.1109/access.2019.2949577]
Shi F Y, Qin W, Chen F, Zhao H Y and Jin X. 2020. Effects of respiratory phase and phantom position on uniformity of 4D-CT image. Chinese Journal of Medical Physics, 37(1):65-68
时飞跃, 秦伟, 陈飞, 赵环宇, 金洵. 2020.呼吸时相和模体位置对4D-CT图像均匀性的影响.中国医学物理学杂志, 37(1):65-68 [DOI:10.3969/j.issn.1005-202X.2020.01.013]
Song W T, Han P, Yang X F, Wang Z and Zhou K F. 2019. Application of 4D-CTA technique in acute pulmonary embolism. Guangxi Medical Journal, 41(1): 96-98
宋维通, 韩鹏, 杨献峰, 王钟, 周科峰. 2019.4D-CTA技术在急性肺动脉栓塞中的应用.广西医学, 41(1): 96-98 [DOI:CNKI:SUN:GYYX.0.2019-01-024http://dx.doi.org/CNKI:SUN:GYYX.0.2019-01-024]
Sundarapandian M, Kalpathi R, Siochi R A C and Kadam A S. 2016. Lung diaphragm tracking in CBCT images using spatio-temporal MRF. Computerized Medical Imaging and Graphics, 53:9-18[DOI:10.1016/j.compmedimag.2016.07.001]
Tao F and Busso C. 2019. End-to-end audiovisual speech activity detection with bimodal recurrent neural models. Speech Communication, 113:25-35[DOI:10.1016/j.specom.2019.07.003]
van de Leemput S C, Meijs M, Patel A, Meijer F J A, van Ginneken B and Manniesing R. 2019. Multiclass brain tissue segmentation in 4D CT using convolutional neural networks. IEEE Access, 7:51557-51569[DOI:10.1109/access.2019.2910348]
Wang B, Prastawa M, Irimia A, Saha A, Liu W, Goh S Y M, Vespa P M, van Horn J D and Gerig G. 2016. Modeling 4D pathological changes by leveraging normative models. Computer Vision and Image Understanding, 151:3-13[DOI:10.1016/j.cviu.2016.01.007]
Wong K C L, Summers R M, Kebebew E and Yao J H. 2017. Pancreatic tumor growth prediction with elastic-growth decomposition, image-derived motion, and FDM-FEM coupling. IEEE Transactions on Medical Imaging, 36(1):111-123[DOI:10.1109/tmi.2016.2597313]
Yang D W, Jia X B, Xiao Y J, Wang X P, Wang Z C and Yang Z H. 2019. Noninvasive evaluation of the pathologic grade of hepatocellular carcinoma using MCF-3DCNN:a pilot study. Biomed Research International, 2019:#9783106[DOI:10.1155/2019/9783106]
Yang Y, van Reeth E, Poh C L, Tan C H and Tham I W K. 2014. A spatiotemporal-based scheme for efficient registration-based segmentation of thoracic 4-D MRI. IEEE Journal of Biomedical and Health Informatics, 18(3):969-977[DOI:10.1109/jbhi.2013.2282183]
Zeng X T, Xu C Y, Zhang Y, Dong G Z and Tang X Y. 2019. Research progress on artificial intelligence in the standardization system construction of medical big data. Beijing Biomedical Engineering, 38(6):639-643
曾晓天, 徐春园, 张勇, 董国昭, 唐晓英. 2019.人工智能在医学大数据标准化体系建设中的研究进展.北京生物医学工程, 38(6):639-643
Zerka F, Barakat S, Walsh S, Bogowicz M, Leijenaar R T H, Jochems A, Miraglio B, Townend D and Lambin P. 2020. Systematic review of privacy-preserving distributed machine learning from federated databases in health care. JCO Clinical Cancer Informatics, (4):184-200[DOI:10.1200/cci.19.00047]
Zhang L, Lu L, Wang X S, Zhu R M, Bagheri M, Summers R M and Yao J H. 2019. Spatio-temporal convolutional LSTMs for tumor growth prediction by learning 4D longitudinal patient data. IEEE Transactions on Medical Imaging, 39(4):1114-1126[DOI:10.1109/tmi.2019.2943841]
Zhao Y, Li X, Zhang W, Zhao S J, Makkie M, Zhang M, Li Q Z and Liu T M. 2018. Modeling 4D fMRI data via spatio-temporal convolutional neural networks (ST-CNN)//Proceedings of the 21st International Conference on Medical Image Computing and Computer-Assisted Intervention. Granada: Springer: 1-8[DOI:10.1007/978-3-030-00931-1_21http://dx.doi.org/10.1007/978-3-030-00931-1_21]
Zhong R, Li H, Zhang S and Liu J J. 2018. Advances on recognizing and managing tumor heterogeneity. Chinese Journal of Lung Cancer, 21(9):712-718
钟睿, 李慧, 张爽, 柳菁菁. 2018.如何认识和处理肿瘤异质性.中国肺癌杂志, 21(9):712-718 [DOI:10.3779/j.issn.1009-3419.2018.09.11]
Zhu H Y and Jin Y C. 2020. Multi-objective evolutionary federated learning. IEEE Transactions on Neural Networks and Learning Systems, 31(4):1310-1322[DOI:10.1109/tnnls.2019.2919699]
相关作者
相关机构
京公网安备11010802024621