计算机科学与探索 ›› 2024, Vol. 18 ›› Issue (2): 301-319.DOI: 10.3778/j.issn.1673-9418.2309033
王一凡,刘静,马金刚,邵润华,陈天真,李明
出版日期:
2024-02-01
发布日期:
2024-02-01
WANG Yifan, LIU Jing, MA Jingang, SHAO Runhua, CHEN Tianzhen, LI Ming
Online:
2024-02-01
Published:
2024-02-01
摘要: 乳腺癌是女性最常见的恶性肿瘤,其早期发现具有决定性意义。乳腺影像学检查在早期发现乳腺癌以及在治疗期间监测与评估方面发挥着重要作用,但人工检测医学影像通常耗时耗力。最近,深度学习算法在早期乳腺癌诊断工作中取得了显著进展。通过梳理近几年的相关文献,对深度学习技术在不同成像模式的乳腺癌诊断中的应用进行了系统综述,旨在为深入开展基于深度学习的乳腺癌诊断研究提供参考。首先概述了乳腺X线摄影、超声影像、磁共振成像和正电子发射计算机断层显像四种乳腺癌成像模式并进行了简要对比,列举了多种成像方式对应的公共数据集。重点对基于上述四种不同成像模式的深度学习架构的不同任务(病变检测、分割和分类)进行了系统的综述,对比分析了各算法性能、改进思路及其优缺点。最后,对现有技术存在的问题进行分析,并针对目前工作的局限性对未来发展方向进行展望。
王一凡, 刘静, 马金刚, 邵润华, 陈天真, 李明. 深度学习在乳腺癌影像学检查中的应用进展[J]. 计算机科学与探索, 2024, 18(2): 301-319.
WANG Yifan, LIU Jing, MA Jingang, SHAO Runhua, CHEN Tianzhen, LI Ming. Application Progress of Deep Learning in Imaging Examination of Breast Cancer[J]. Journal of Frontiers of Computer Science and Technology, 2024, 18(2): 301-319.
[1] SIEGEL R L, MILLER K D, WAGLE N S, et al. Cancer statistics, 2023[J]. CA: A Cancer Journal for Clinicians, 2023, 73(1): 17-48. [2] ZHOU X, LI C, RAHAMAN M M, et al. A comprehensive review for breast histopathology image analysis using classical and deep neural networks[J]. IEEE Access, 2020, 8: 90931-90956. [3] 师金, 梁迪, 李道娟, 等. 全球女性乳腺癌流行情况研究[J]. 中国肿瘤, 2017, 26(9): 683-690. SHI J, LIANG D, LI D J, et al. Epidemiological status of global female breast cancer[J]. China Cancer, 2017, 26(9): 683-690. [4] CRUZ-ROA A, GILMORE H, BASAVANHALLY A, et al. Accurate and reproducible invasive breast cancer detection in whole-slide images: a deep learning approach for quantifying tumor extent[J]. Scientific Reports, 2017, 7(1): 1-14. [5] 陈智丽, 高皓, 潘以轩, 等. 乳腺X线图像计算机辅助诊断技术综述[J]. 计算机工程与应用, 2022, 58(4): 1-21. CHEN Z L, GAO H, PAN Y X, et al. Review of computer aided diagnosis technology in mammography[J]. Computer Engineering and Applications, 2022, 58(4): 1-21. [6] HAMIDINEKOO A, DENTON E, RAMPUN A, et al. Deep learning in mammography and breast histology, an overview and future trends[J]. Medical Image Analysis, 2018, 47: 45-67. [7] LECUN Y, BENGIO Y. Convolutional networks for images, speech, and time series[J]. The Handbook of Brain Theory and Neural Networks, 1995(10): 1-14. [8] GAO Y, LIN J, ZHOU Y, et al. The application of traditional machine learning and deep learning techniques in mammography: a review[J]. Frontiers in Oncology, 2023, 13: 1213045. [9] PENGIRAN MOHAMAD D N F, MASHOHOR S, MAHMUD R, et al. Transition of traditional method to deep learning based computer-aided system for breast cancer using automated breast ultrasound system (ABUS) images: a review[J]. Artificial Intelligence Review, 2023, 56(12): 15271-15300. [10] ZHAO X, BAI J W, GUO Q, et al. Clinical applications of deep learning in breast MRI[J]. Biochimica et Biophysica Acta (BBA)-Reviews on Cancer, 2023: 188864. [11] 欧阳汝珊, 林小慧, 马捷. 基于深度学习的乳腺X线摄影的临床应用价值[J]. 国际医学放射学杂志, 2021, 44(6): 673-677. OUYANG R S, LIN X H, MA J. Clinical value of deep learning-based mammography[J]. International Journal of Medical Radiology, 2021, 44(6): 673-677. [12] 张晓栋, 张光. 基于磁共振图像的深度学习方法在乳腺癌中的研究进展[J]. 临床放射学杂志, 2023, 42(7): 1197-1200. ZHANG X D, ZHANG G. Research progress of deep learning method based on MRI in breast cancer[J]. Journal of Clinical Radiology, 2023, 42(7): 1197-1200. [13] 中国抗癌协会乳腺癌诊治指南与规范(2021年版)[J]. 中国癌症杂志, 2021, 31(10): 954-1040. CACA guideline for breast cancer(2021)[J]. China Oncology, 2021, 31(10): 954-1040. [14] MICHELL M J, IQBAL A, WASAN R K, et al. A comparison of the accuracy of film-screen mammography, full-field digital mammography, and digital breast tomosynthesis[J]. Clinical Radiology, 2012, 67(10): 976-981. [15] PISANO E D, GATSONIS C, HENDRICK E, et al. Diagnostic performance of digital versus film mammography for breast-cancer screening[J]. New England Journal of Medicine, 2005, 353(17): 1773-1783. [16] HEDDSON B, R?NNOW K, OLSSON M, et al. Digital versus screen-film mammography: a retrospective comparison in a population-based screening program[J]. European Journal of Radiology, 2007, 64(3): 419-425. [17] 戴东. 国产乳腺专用PET(PEM)对乳腺癌诊断价值的临床研究[D]. 天津: 天津医科大学, 2016. DAI D. The clinical study of domestic dedicated positron emission mammography in the diagnosis of breast cancer[D]. Tianjin: Tianjin Medical University, 2016. [18] SKAANE P, BANDOS A I, GULLIEN R, et al. Comparison of digital mammography alone and digital mammography plus tomosynthesis in a population-based screening program[J]. Radiology, 2013, 267(1): 47-56. [19] BURT J R, TOROSDAGLI N, KHOSRAVAN N, et al. Deep learning beyond cats and dogs: recent advances in diagnosing breast cancer with deep neural networks[J]. The British Journal of Radiology, 2018, 91(1089): 20170545. [20] SHARMA S, MEHRA R. Conventional machine learning and deep learning approach for multi-classification of breast cancer histopathology images—a comparative insight[J]. Journal of Digital Imaging, 2020, 33: 632-654. [21] HEATH M, BOWYER K, KOPANS D, et al. Current status of the digital database for screening mammography[J]. Digital Mammography, 1998, 13: 457-460. [22] 中国抗癌协会乳腺癌诊治指南与规范(2019年版)[J]. 中国癌症杂志, 2019, 29(8): 609-680. CACA guideline for breast cancer(2019)[J]. China Oncology, 2019, 29(8): 609-680. [23] LEE R S, GIMENEZ F, HOOGI A, et al. A curated mammography data set for use in computer-aided detection and diagnosis research[J]. Scientific Data, 2017, 4(1): 1-9. [24] SUCKLING J,PARKER J,DANCE D. Mammographic image analysis society (MIAS) database v1.21[EB/OL]. (2021-06-05)[2023-08-02]. https://www.repository.cam.ac.uk/handle/1810/250394. [25] OLIVER A, FREIXENET J, MARTI J, et al. A review of automatic mass detection and segmentation in mammographic images[J]. Medical Image Analysis, 2010, 14(2): 87-110. [26] DOMINGUEZ A R, NANDI A K. Detection of masses in mammograms using enhanced multilevel-thresholding segmentation and region selection based on rank[C]//Proceedings of the 2007 IASTED International Conference on Biomedical Engineering, Innsbruck, Feb 14-16, 2007: 370-375. [27] MOREIRA I C, AMARAL I, DOMINGUES I, et al. Inbreast: toward a full-field digital mammographic database[J]. Academic Radiology, 2012, 19(2): 236-248. [28] MATHEUS B R N, SCHIABEL H. Online mammographic images database for development and comparison of CAD schemes[J]. Journal of Digital Imaging, 2011, 24: 500-506. [29] FITZGERALD R. Error in radiology[J]. Clinical Radiology,2001. DOI: 10.1053/crad.2001.0858. [30] LANDHUIS E. Deep learning takes on tumours[J]. Nature, 2020, 580(7804): 551-554. [31] RIBLI D, HORVáTH A, UNGER Z, et al. Detecting and classifying lesions in mammograms with deep learning[J]. Scientific Reports, 2018, 8(1): 4165. [32] SUN L, SUN H, WANG J, et al. Breast mass detection in mammography based on image template matching and CNN[J]. Sensors, 2021, 21(8): 2855. [33] GARRUCHO L, KUSHIBAR K, JOUIDE S, et al. Domain generalization in deep learning based mass detection in mammography: a large-scale multi-center study[J]. Artificial Intelligence in Medicine, 2022, 132: 102386. [34] REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, real-time object detection[C]//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, Jun 27-30, 2016. Washington: IEEE Computer Society, 2016: 779-788. [35] AL-ANTARI M A, AL-MASNI M A, CHOI M T, et al. A fully integrated computer-aided diagnosis system for digital X-ray mammograms via deep learning detection, segmentation, and classification[J]. International Journal of Medical Informatics, 2018, 117: 44-54. [36] SU Y, LIU Q, XIE W, et al. YOLO-LOGO: a transformer-based YOLO segmentation model for breast mass detection and segmentation in digital mammograms[J]. Computer Methods and Programs in Biomedicine, 2022, 221: 106903. [37] HONJO T, UEDA D, KATAYAMA Y, et al. Visual and quantitative evaluation of microcalcifications in mammograms with deep learning-based super-resolution[J]. European Journal of Radiology, 2022, 154: 110433. [38] UEDA D, YAMAMOTO A, ONODA N, et al. Development and validation of a deep learning model for detection of breast cancers in mammography from multi-institutional data-sets[J]. PLoS One, 2022, 17(3): e0265751. [39] LIN T Y, GOYAL P, GIRSHICK R, et al. Focal loss for dense object detection[C]//Proceedings of the 2017 IEEE International Conference on Computer Vision, Venice, Oct 22-29, 2017. Washington: IEEE Computer Society, 2017: 2980-2988. [40] HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, Jun 26-Jul 1, 2016. Washington: IEEE Computer Society, 2016: 770-778. [41] LIN T Y, DOLLáR P, GIRSHICK R, et al. Feature pyramid networks for object detection[C]//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, Jul 21-26, 2017. Washington: IEEE Computer Society, 2017: 2117-2125. [42] BECKER A S, MARCON M, GHAFOOR S, et al. Deep learning in mammography: diagnostic accuracy of a multipurpose image analysis software in the detection of breast cancer[J]. Investigative Radiology, 2017, 52(7): 434-440. [43] RODRíGUEZ-RUIZ A, KRUPINSKI E, MORDANG J J, et al. Detection of breast cancer with mammography: effect of an artificial intelligence support system[J]. Radiology, 2019, 290(2): 305-314. [44] RONNEBERGER O, FISCHER P, BROX T. U-net: convolutional networks for biomedical image segmentation[C]//LNCS 9351: Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Oct 5-9, 2015. Cham: Springer, 2015: 234-241. [45] SOULAMI K B, KAABOUCH N, SAIDI M N, et al. Breast cancer: one-stage automated detection, segmentation, and classification of digital mammograms using UNet model based-semantic segmentation[J]. Biomedical Signal Processing and Control, 2021, 66: 102481. [46] SALAMA W M, ALY M H. Deep learning in mammography images segmentation and classification: automated CNN approach[J]. Alexandria Engineering Journal, 2021, 60(5): 4701-4709. [47] ALKHALEEFAH M, TAN T H, CHANG C H, et al. Connected-SegNets: a deep learning model for breast tumor Segmentation from X-ray images[J]. Cancers, 2022, 14(16): 4030. [48] AHMED L, IQBAL M M, ALDABBAS H, et al. Images data practices for semantic segmentation of breast cancer using deep neural network[J]. Journal of Ambient Intelligence and Humanized Computing, 2020(1). [49] ZHOU K, LI W, ZHAO D. Deep learning-based breast region extraction of mammographic images combining pre-processing methods and semantic segmentation supported by Deeplab v3+[J]. Technology and Health Care, 2022, 30(S1): 173-190. [50] KAVITHA T, MATHAI P P, KARTHIKEYAN C, et al. Deep learning based capsule neural network model for breast cancer diagnosis using mammogram images[J]. Interdisciplinary Sciences: Computational Life Sciences, 2022, 14: 113-129. [51] KAPUR J N, SAHOO P K, WONG A K C. A new method for gray-level picture thresholding using the entropy of the histogram[J]. Computer Vision, Graphics, and Image Processing, 1985, 29(3): 273-285. [52] MALEBARY S J, HASHMI A. Automated breast mass classification system using deep learning and ensemble learning in digital mammogram[J]. IEEE Access, 2021, 9: 55312-55328. [53] SAMEE N A, ATTEIA G, MESHOUL S, et al. Deep learning cascaded feature selection framework for breast cancer classification: hybrid CNN with univariate-based approach[J]. Mathematics, 2022, 10(19): 3631. [54] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks[C]//Advances in Neural Information Processing Systems 25,Lake Tahoe, Dec 3-6, 2012: 1106-1114. [55] SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[J]. arXiv:1409.1556, 2014. [56] SZEGEDY C, LIU W, JIA Y, et al. Going deeper with convolutions[C]//Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition, Boston, Jun 7-12, 2015. Washington: IEEE Computer Society, 2015: 1-9. [57] BACCOUCHE A, GARCIA-ZAPIRAIN B, ELMAGHRABY A S. An integrated framework for breast mass classification and diagnosis using stacked ensemble of residual neural networks[J]. Scientific Reports, 2022, 12(1): 12259. [58] CAI H, HUANG Q, RONG W, et al. Breast microcalcification diagnosis using deep convolutional neural network from digital mammograms[J]. Computational and Mathematical Methods in Medicine, 2019: 2717454. [59] T?RYAK? V M. Deep transfer learning to classify mass and calcification pathologies from screen film mammograms[J]. Bitlis Eren üniversitesi Fen Bilimleri Dergisi, 2023, 12(1): 57-65. [60] YAP M H, PONS G, MARTI J, et al. Automated breast ulra-sound lesions detection using convolutional neural networks[J]. IEEE Journal of Biomedical and Health Informatics, 2017, 22(4): 1218-1226. [61] BALAHA H M, SAIF M, TAMER A, et al. Hybrid deep learning and genetic algorithms approach (HMBDLGAHA) for the early ultrasound diagnoses of breast cancer[J]. Neural Computing and Applications, 2022, 34(11): 8671-8695. [62] ZHANG Z, LI Y, WU W, et al. Tumor detection using deep learning method in automated breast ultrasound[J]. Biomedical Signal Processing and Control, 2021, 68: 102677. [63] ZHOU Y, CHEN H, LI Y, et al. 3D multi-view tumor detection in automated whole breast ultrasound using deep convolutional neural network[J]. Expert Systems with Applications, 2021, 168: 114410. [64] MALEKMOHAMMADI A, BAREKATREZAEI S, KOZEGAR E, et al. Mass detection in automated 3-D breast ultrasound using a patch Bi-ConvLSTM network[J]. Ultrasonics, 2023, 129: 106891. [65] BYRA M, GALPERIN M, OJEDA-FOURNIER H, et al. Breast mass classification in sonography with transfer learning using a deep convolutional neural network and color conversion[J]. Medical Physics, 2019, 46(2): 746-755. [66] FUJIOKA T, KUBOTA K, MORI M, et al. Virtual interpolation images of tumor development and growth on breast ulra-sound image synthesis with deep convolutional generative adversarial networks[J]. Journal of Ultrasound in Medicine, 2021, 40(1): 61-69. [67] HAN L, HUANG Y, DOU H, et al. Semi-supervised segmentation of lesion from breast ultrasound images with attentional generative adversarial network[J]. Computer Methods and Programs in Biomedicine, 2020, 189: 105275. [68] LI Y, LIU Y, HUANG L, et al. Deep weakly-supervised breast tumor segmentation in ultrasound images with explicit anatomical constraints[J]. Medical Image Analysis, 2022, 76: 102315. [69] ALMAJALID R, SHAN J, DU Y, et al. Development of a deep-learning-based method for breast ultrasound image segmentation[C]//Proceedings of the 2018 17th IEEE International Conference on Machine Learning and Applications, Orlando, Dec 17-20, 2018. Piscataway: IEEE, 2018: 1103-1108. [70] SANNASI CHAKRAVARTHY S R, RAJAGURU H. SKMAT-U-Net architecture for breast mass segmentation[J]. International Journal of Imaging Systems and Technology, 2022, 32(6): 1880-1888. [71] BYRA M, JAROSIK P, DOBRUCH-SOBCZAK K, et al. Joint segmentation and classification of breast masses based on ultrasound radio-frequency data and convolutional neural networks[J]. Ultrasonics, 2022, 121: 106682. [72] RAGAB M, ALBUKHARI A, ALYAMI J, et al. Ensemble deep-learning-enabled clinical decision support system for breast cancer diagnosis and classification on ultrasound images[J]. Biology, 2022, 11(3): 439. [73] AL-DHABYANI W, GOMAA M, KHALED H, et al. Dataset of breast ultrasound images[J]. Data in Brief, 2020, 28: 104863. [74] HAN S, KANG H K, JEONG J Y, et al. A deep learning framework for supporting the classification of breast lesions in ultrasound images[J]. Physics in Medicine & Biology, 2017, 62(19): 7714. [75] KAPLAN E, CHAN W Y, DOGAN S, et al. Automated BI-RADS classification of lesions using pyramid triple deep feature generator technique on breast ultrasound images[J]. Medical Engineering & Physics, 2022, 108: 103895. [76] DING W, WANG J, ZHOU W, et al. Joint localization and classification of breast cancer in B-Mode ultrasound imaging via collaborative learning with elastography[J]. IEEE Journal of Biomedical and Health Informatics, 2022, 26(9): 4474-4485. [77] JABEEN K, KHAN M A, ALHAISONI M, et al. Breast cancer classification from ultrasound images using probability-based optimal deep learning feature fusion[J]. Sensors, 2022, 22(3): 807. [78] RAZA A, ULLAH N, KHAN J A, et al. DeepBreastCancerNet: a novel deep learning model for breast cancer detection using ultrasound images[J]. Applied Sciences, 2023, 13(4): 2082. [79] MAICAS G, CARNEIRO G, BRADLEY A P, et al. Deep reinforcement learning for active breast lesion detection from DCE-MRI[C]//LNCS 10435: Proceedings of the 20th International Conference on Medical Image Computing and Computer-Assisted Intervention, Quebec, Sep 11-13, 2017. Cham: Springer, 2017: 665-673. [80] ZHOU J, ZHANG Y, CHANG K T, et al. Diagnosis of benign and malignant breast lesions on DCE-MRI by using radiomics and deep learning with consideration of peritumor tissue[J]. Journal of Magnetic Resonance Imaging, 2020, 51(3): 798-809. [81] DAIMIEL NARANJO I, GIBBS P, REINER J S, et al. Breast lesion classification with multiparametric breast MRI using radiomics and machine learning: a comparison with radiologists’ performance[J]. Cancers, 2022, 14(7): 1743. [82] ADACHI M, FUJIOKA T, MORI M, et al. Detection and dig-nosis of breast cancer using artificial intelligence based assessment of maximum intensity projection dynamic contrast-enhanced magnetic resonance images[J]. Diagnostics, 2020, 10(5): 330. [83] AYATOLLAHI F, SHOKOUHI S B, MANN R M, et al. Auto-matic breast lesion detection in ultrafast DCE-MRI using deep learning[J]. Medical Physics, 2021, 48(10): 5897-5907. [84] ZHANG Y, LIU Y L, NIE K, et al. Deep learning-based auto-matic diagnosis of breast cancer on MRI using mask R-CNN for detection followed by ResNet50 for classification[J]. Academic Radiology, 2023, 30(S2): S161-S171. [85] ZHANG J, SAHA A, ZHU Z, et al. Hierarchical convolutional neural networks for segmentation of breast tumors in MRI with application to radiogenomics[J]. IEEE Transactions on Medical Imaging, 2018, 38(2): 435-447. [86] LU W, WANG Z, HE Y, et al. Breast cancer detection based on merging four modes MRI using convolutional neural networks[C]//Proceedings of the 2019 IEEE International Conference on Acoustics, Speech and Signal Processing , Brighton, May 12-17, 2019. Piscataway: IEEE, 2019: 1035-1039. [87] YUE W, ZHANG H, ZHOU J, et al. Deep learning-based automatic segmentation for size and volumetric measurement of breast cancer on magnetic resonance imaging[J]. Frontiers in Oncology, 2022. DOI: 10.3389/fonc.2022.984626. [88] LIU X, ZHANG Y, JING H, et al. Ore image segmentation method using U-Net and Res_Unet convolutional networks[J]. RSC Advances, 2020, 10(16): 9396-9406. [89] CARVALHO E D, SILVA R R V, MATHEW M J, et al. Tumor segmentation in breast DCE-MRI slice using deep learning methods[C]//Proceedings of the 2021 IEEE Symposium on Computers and Communications, Athens, Sep 5-8, 2021. Piscataway: IEEE, 2021: 1-6. [90] BOUCHEBBAH F, SLIMANI H. 3D automatic levels propagation approach to breast MRI tumor segmentation[J]. Expert Systems with Applications, 2021, 165: 113965. [91] LIU M Z, SWINTELSKI C, SUN S, et al. Weakly supervised deep learning approach to breast MRI assessment[J]. Academic Radiology, 2022, 29: S166-S172. [92] JING X, WIELEMA M, CORNELISSEN L J, et al. Using deep learning to safely exclude lesions with only ultrafast breast MRI to shorten acquisition and reading time[J]. European Radiology, 2022, 32(12): 8706-8715. [93] VERBURG E, VAN GILS C H, VAN DER VELDEN B H M, et al. Deep learning for automated triaging of 4581 breast MRI examinations from the DENSE trial[J]. Radiology, 2022, 302(1): 29-36. [94] ZHU Z, ALBADAWY E, SAHA A, et al. Deep learning for identifying radiogenomic associations in breast cancer[J]. Computers in Biology and Medicine, 2019, 109: 85-90. [95] ZHANG Y, CHEN J H, LIN Y, et al. Prediction of breast cancer molecular subtypes on DCE-MRI using convolutional neural network with transfer learning between two centers[J]. European Radiology, 2021, 31: 2559-2567. [96] MORRIS E A, COMSTOCK C E, LEE C H, et al. ACR BI-RADS? magnetic resonance imaging[J]. ACR BI-RADS? Atlas, Breast Imaging Reporting and Data System, 2013, 5. [97] GIESS C S, YEH E D, RAZA S, et al. Background parenchymal enhancement at breast MR imaging: normal patterns, diagnostic challenges, and potential for false-positive and false-negative interpretation[J]. Radiographics, 2014, 34(1): 234-247. [98] BORKOWSKI K, ROSSI C, CIRITSIS A, et al. Fully automatic classification of breast MRI background parenchymal enhancement using a transfer learning approach[J]. Medicine, 2020, 99(29): e21243. [99] ESKREIS-WINKLER S, SUTTON E J, D’ALESSIO D, et al. Breast MRI background parenchymal enhancement categorization using deep learning: outperforming the radiologist[J]. Journal of Magnetic Resonance Imaging, 2022, 56(4): 1068-1076. [100] MORI M, FUJIOKA T, KATSUTA L, et al. Diagnostic performance of time-of-flight PET/CT for evaluating nodal metastasis of the axilla in breast cancer[J]. Nuclear Medicine Communications, 2019, 40(9): 958-964. [101] ISHIBA T, NAKAGAWA T, SATO T, et al. Efficiency of fluorodeoxyglucose positron emission tomography/computed tomography to predict prognosis in breast cancer patients received neoadjuvant chemotherapy[J]. SpringerPlus, 2015, 4(1): 1-9. [102] TAKAHASHI K, FUJIOKA T, OYAMA J, et al. Deep learning using multiple degrees of maximum-intensity projection for PET/CT image classification in breast cancer[J]. Tomography, 2022, 8(1): 131-141. [103] WEBER M, KERSTING D, UMUTLU L, et al. Just another “Clever Hans”? Neural networks and FDG PET-CT to predict the outcome of patients with breast cancer[J]. European Journal of Nuclear Medicine and Molecular Imaging, 2021, 48: 3141-3150. [104] MOREAU N, ROUSSEAU C, FOURCADE C, et al. Deep learning approaches for bone and bone lesion segmentation on 18FDG PET/CT imaging in the context of metastatic breast cancer[C]//Proceedings of the 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, Montreal, Jul 20-24, 2020. Piscataway: IEEE, 2020: 1532-1535. [105] ZHANG Z, XIE Y, XING F, et al. MDNET: a semantically and visually interpretable medical image diagnosis network[C]//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, Jul 21-26, 2017. Washington: IEEE Computer Society, 2017: 6428-6436. |
[1] | 薛金强, 吴秦. 面向图像复原和增强的轻量级交叉门控Transformer[J]. 计算机科学与探索, 2024, 18(3): 718-730. |
[2] | 杨超城, 严宣辉, 陈容均, 李汉章. 融合双重注意力机制的时间序列异常检测模型[J]. 计算机科学与探索, 2024, 18(3): 740-754. |
[3] | 申通, 王硕, 李孟, 秦伦明. 深度学习在动物行为分析中的应用研究进展[J]. 计算机科学与探索, 2024, 18(3): 612-626. |
[4] | 考文涛, 李明, 马金刚. 卷积神经网络在结直肠息肉辅助诊断中的应用综述[J]. 计算机科学与探索, 2024, 18(3): 627-645. |
[5] | 彭斌, 白静, 李文静, 郑虎, 马向宇. 面向图像分类的视觉Transformer研究进展[J]. 计算机科学与探索, 2024, 18(2): 320-344. |
[6] | 王昆, 郭威, 王尊严, 韩文强. 赤足足迹识别研究综述[J]. 计算机科学与探索, 2024, 18(1): 44-57. |
[7] | 高洁, 赵心馨, 于健, 徐天一, 潘丽, 杨珺, 喻梅, 李雪威. 结合密度图回归与检测的密集计数研究[J]. 计算机科学与探索, 2024, 18(1): 127-137. |
[8] | 刘华玲, 陈尚辉, 曹世杰, 朱建亮, 任青青. 基于多模态学习的虚假新闻检测研究[J]. 计算机科学与探索, 2023, 17(9): 2015-2029. |
[9] | 赵婷婷, 孙威, 陈亚瑞, 王嫄, 杨巨成. 潜在空间中深度强化学习方法研究综述[J]. 计算机科学与探索, 2023, 17(9): 2047-2074. |
[10] | 徐光宪, 冯春, 马飞. 基于UNet的医学图像分割综述[J]. 计算机科学与探索, 2023, 17(8): 1776-1792. |
[11] | 季长清, 王兵兵, 秦静, 汪祖民. 深度特征的实例图像检索算法综述[J]. 计算机科学与探索, 2023, 17(7): 1565-1575. |
[12] | 吴水秀, 罗贤增, 熊键, 钟茂生, 王明文. 知识追踪研究综述[J]. 计算机科学与探索, 2023, 17(7): 1506-1525. |
[13] | 马妍, 古丽米拉·克孜尔别克. 图像语义分割方法在高分辨率遥感影像解译中的研究综述[J]. 计算机科学与探索, 2023, 17(7): 1526-1548. |
[14] | 张如琳, 王海龙, 柳林, 裴冬梅. 音乐自动标注分类方法研究综述[J]. 计算机科学与探索, 2023, 17(6): 1225-1248. |
[15] | 曹斯铭, 王晓华, 王弘堃, 曹轶. MSV-Net:面向科学模拟面体混合数据的超分重建方法[J]. 计算机科学与探索, 2023, 17(6): 1321-1328. |
阅读次数 | ||||||
全文 |
|
|||||
摘要 |
|
|||||