nach oben

Journal of Medical Systems

Erschienen in:

01.11.2018 | Image & Signal Processing

Medical Image Analysis using Convolutional Neural Networks: A Review

verfasst von: Syed Muhammad Anwar, Muhammad Majid, Adnan Qayyum, Muhammad Awais, Majdi Alnowami, Muhammad Khurram Khan

Erschienen in: Journal of Medical Systems | Ausgabe 11/2018

Einloggen, um Zugang zu erhalten

Abstract

The science of solving clinical problems by analyzing images generated in clinical practice is known as medical image analysis. The aim is to extract information in an affective and efficient manner for improved clinical diagnosis. The recent advances in the field of biomedical engineering have made medical image analysis one of the top research and development area. One of the reasons for this advancement is the application of machine learning techniques for the analysis of medical images. Deep learning is successfully used as a tool for machine learning, where a neural network is capable of automatically learning features. This is in contrast to those methods where traditionally hand crafted features are used. The selection and calculation of these features is a challenging task. Among deep learning techniques, deep convolutional networks are actively used for the purpose of medical image analysis. This includes application areas such as segmentation, abnormality detection, disease classification, computer aided diagnosis and retrieval. In this study, a comprehensive review of the current state-of-the-art in medical image analysis using deep convolutional networks is presented. The challenges and potential of these techniques are also highlighted.

Greenspan, H., van Ginneken, B., and Summers, R. M., Guest editorial deep learning in medical imaging: Overview and future promise of an exciting new technique. IEEE Trans. Med. Imaging 35(5):1153–1159, 2016.CrossRef

Wang, G., A perspective on deep imaging. IEEE Access 4:8914–8924, 2016.CrossRef

Liu, Y., Cheng, H., Huang, J., Zhang, Y., Tang, X., Tian, J.-W., and Wang, Y., Computer aided diagnosis system for breast cancer based on color doppler flow imaging. J. Med. Syst. 36(6):3975–3982, 2012.PubMedCrossRef

Diao, X.-F., Zhang, X.-Y., Wang, T.-F., Chen, S.-P., Yang, Y., and Zhong, L., Highly sensitive computer aided diagnosis system for breast tumor based on color doppler flow images. J. Med. Syst. 35(5):801–809, 2011.PubMedCrossRef

Wan, J., Wang, D., Hoi, S. C. H., Wu, P., Zhu, J., Zhang, Y., and Li, J.: Deep learning for content-based image retrieval: A comprehensive study. In: Proceedings of the 22nd ACM international conference on Multimedia. ACM, pp. 157–166, 2014

Deng, L., Yu, D., et al., Deep learning: Methods and applications. Foundations and Trends®, in Signal Processing 7(3–4):197–387, 2014.CrossRef

Shi, S., Wang, Q., Xu, P., and Chu, X.: Benchmarking state-of-the-art deep learning software tools. In: 2016 7th International Conference on Cloud Computing and Big Data (CCBD). IEEE, pp. 99–104, 2016

Janowczyk, A., and Madabhushi, A.: Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases, Journal of pathology informatics 7

Lakhani, P., Gray, D. L., Pett, C. R., Nagy, P., and Shih, G., Hello world deep learning in medical imaging. J. Digit. Imaging 31(3):283–289, 2018.PubMedCentralCrossRef

10.

Heidenreich, A., Desgrandschamps, F., and Terrier, F., Modern approach of diagnosis and management of acute flank pain: Review of all imaging modalities. Eur. Urol. 41(4):351–362, 2002.PubMedCrossRef

11.

Rahman, M. M., Desai, B.C., and Bhattacharya, P., Medical image retrieval with probabilistic multi-class support vector machine classifiers and adaptive similarity fusion. Comput. Med. Imaging Graph. 32(2):95–108, 2008.PubMedCrossRef

12.

Sáez, A., Sánchez-Monedero, J., Gutiérrez, P. A., and Hervás-Martínez, C., Machine learning methods for binary and multiclass classification of melanoma thickness from dermoscopic images. IEEE Trans. Med. Imaging 35(4):1036–1045, 2016.PubMedCrossRef

13.

Miri, M. S., Abràmoff, M. D., Lee, K., Niemeijer, M., Wang, J.-K., Kwon, Y. H., and Garvin, M. K., Multimodal segmentation of optic disc and cup from sd-oct and color fundus photographs using a machine-learning graph-based approach. IEEE Trans. Med. Imaging 34(9):1854–1866, 2015.PubMedPubMedCentralCrossRef

14.

Gao, Y., Zhan, Y., and Shen, D., Incremental learning with selective memory (ilsm): Towards fast prostate localization for image guided radiotherapy. IEEE Trans. Med. Imaging 33(2):518–534, 2014.PubMedPubMedCentralCrossRef

15.

Tao, Y., Peng, Z., Krishnan, A., and Zhou, X. S., Robust learning-based parsing and annotation of medical radiographs. IEEE Trans. Med. Imaging 30(2):338–350, 2011.PubMedCrossRef

16.

Ahmad, J., Muhammad, K., Lee, M. Y., and Baik, S. W., Endoscopic image classification and retrieval using clustered convolutional features. J. Med. Syst. 41(12):196, 2017.PubMedCrossRef

17.

Ahmad, J., Muhammad, K., and Baik, S. W., Medical image retrieval with compact binary codes generated in frequency domain using highly reactive convolutional features. J. Med. Syst. 42(2):24, 2018.CrossRef

18.

Jenitta, A., and Ravindran, R. S., Image retrieval based on local mesh vector co-occurrence pattern for medical diagnosis from mri brain images. J. Med. Syst. 41(10):157, 2017.PubMedCrossRef

19.

Zhang, L., and Ji, Q., A bayesian network model for automatic and interactive image segmentation. IEEE Trans. Image Process. 20(9):2582–2593, 2011.PubMedCrossRef

20.

Sharma, M. M.: Brain tumor segmentation techniques: A survey. Brain 4 (4): 220–223

21.

Vishnuvarthanan, G., Rajasekaran, M. P., Subbaraj, P., and Vishnuvarthanan, A., An unsupervised learning method with a clustering approach for tumor identification and tissue segmentation in magnetic resonance brain images. Appl. Soft Comput. 38:190–212, 2016.CrossRef

22.

Feng, Y., Zhao, H., Li, X., Zhang, X., and Li, H., A multi-scale 3d otsu thresholding algorithm for medical image segmentation. Digital Signal Process. 60:186–199, 2017.CrossRef

23.

Gupta, D., and Anand, R., A hybrid edge-based segmentation approach for ultrasound medical images. Biomed. Signal Process. Control 31:116–126, 2017.CrossRef

24.

von Landesberger, T., Basgier, D., and Becker, M., Comparative local quality assessment of 3d medical image segmentations with focus on statistical shape model-based algorithms. IEEE Trans. Vis. Comput. Graph. 22 (12):2537–2549, 2016.CrossRef

25.

Anwar, S., Yousaf, S., and Majid, M.: Brain timor segmentation on multimodal mri scans using emap algorithm. In: Engineering in medicine and biology soceity (EMBC), International Conference of the IEEE. IEEE, pp. 1-4, 2018

26.

Cabria, I., and Gondra, I., Mri segmentation fusion for brain tumor detection. Information Fusion 36:1–9, 2017.CrossRef

27.

Soulami, K. B., Saidi, M. N., and Tamtaoui, A.: A cad system for the detection of abnormalities in the mammograms using the metaheuristic algorithm particle swarm optimization (pso). In: Advances in Ubiquitous Networking 2. Springer, pp. 505–517, 2017

28.

Kobayashi, Y., Kobayashi, H., Giles, J. T., Yokoe, I., Hirano, M., Nakajima, Y., and Takei, M., Detection of left ventricular regional dysfunction and myocardial abnormalities using complementary cardiac magnetic resonance imaging in patients with systemic sclerosis without cardiac symptoms: A pilot study. Intern. Med. 55(3): 237–243, 2016.PubMedCrossRef

29.

Mosquera-Lopez, C., Agaian, S., Velez-Hoyos, A., and Thompson, I., Computer-aided prostate cancer diagnosis from digitized histopathology: A review on texture-based systems. IEEE Rev. Biomed. Eng. 8:98–113, 2015.PubMedCrossRef

30.

Ma, H.-Y., Zhou, Z., Wu, S., Wan, Y.-L., and Tsui, P.-H., A computer-aided diagnosis scheme for detection of fatty liver in vivo based on ultrasound kurtosis imaging. J. Med. Syst. 40(1):33, 2016.PubMedCrossRef

31.

Remeseiro, B., Mosquera, A., and Penedo, M. G., Casdes: A computer-aided system to support dry eye diagnosis based on tear film maps. IEEE journal of biomedical and health informatics 20(3):936–943, 2016.PubMedCrossRef

32.

Torrents-Barrena, J., Lazar, P., Jayapathy, R., Rathnam, M., Mohandhas, B., and Puig, D., Complex wavelet algorithm for computer-aided diagnosis of alzheimer’s disease. Electron. Lett. 51(20):1566–1568, 2015.CrossRef

33.

Saha, M., Mukherjee, R., and Chakraborty, C., Computer-aided diagnosis of breast cancer using cytological images: A systematic review. Tissue Cell 48(5):461–474, 2016.PubMedCrossRef

34.

Salam, A. A., Akram, M. U., Wazir, K., Anwar, S. M., and Majid, M.: Autonomous glaucoma detection from fundus image using cup to disc ratio and hybrid features. In: IEEE International Symposium on Signal processing and information technology (ISSPIT) 2015. IEEE, pp. 370-374, 2015

35.

Salam, A. A., Akram, M. U., Abbas, S., and Anwar, S. M.: Optic disc localization using local vessel based features and support vector machine. In: IEEE 15th International Conference on Bioinformatics and Bioengineering (BIBE), 2015. IEEE, pp. 1–6, 2015

36.

Altaf, T., Anwar, S. M., Gul, N., Majeed, M. N., and Majid, M., Multi-class alzheimer’s disease classification using image and clinical features. Biomed. Signal Process. Control 43:64–74, 2018.CrossRef

37.

Hwang, K. H., Lee, H., and Choi, D., Medical image retrieval: Past and present. Healthcare informatics research 18(1):3–9, 2012.PubMedPubMedCentralCrossRef

38.

Müller, H., Rosset, A., Vallée, J.-P., Terrier, F., and Geissbuhler, A., A reference data set for the evaluation of medical image retrieval systems. Comput. Med. Imaging Graph. 28(6):295–305, 2004.PubMedCrossRef

39.

Müller, H., Michoux, N., Bandon, D., and Geissbuhler, A., A review of content-based image retrieval systems in medical applications—clinical benefits and future directions. Int. J. Med. Inform. 73(1):1–23, 2004.PubMedCrossRef

40.

Mizotin, M., Benois-Pineau, J., Allard, M., and Catheline, G.: Feature-based brain mri retrieval for alzheimer disease diagnosis. In: 2012 19th IEEE International Conference on Image Processing (ICIP). IEEE, pp. 1241–1244, 2012

41.

Brahmi, D., and Ziou, D.: Improving cbir systems by integrating semantic features. In: 2004 Proceedings of the 1st Canadian Conference on Computer and robot vision. IEEE, pp. 233-240, 2004

42.

Chang, N.-S., and Fu, K.-S., Query-by-pictorial-example. IEEE Trans. Softw. Eng. SE-6(6):519–524, 1980.CrossRef

43.

Thakur, M. S., and Singh, M., Content based image retrieval using line edge singular value pattern (lesvp): A review paper. International Journal of Advanced Research in Computer Science and Software Engineering 5(3): 648–652, 2015.

44.

Jiji, G. W., and Raj, P. S. J. D., Content-based image retrieval in dermatology using intelligent technique. IET Image Process. 9(4):306–317, 2014.CrossRef

45.

Rahman, M. M., Antani, S. K., and Thoma, G. R., A learning-based similarity fusion and filtering approach for biomedical image retrieval using svm classification and relevance feedback. IEEE Trans. Inf. Technol. Biomed. 15(4):640–646, 2011.PubMedCrossRef

46.

Anwar, S. M., Arshad, F., and Majid, M.: Fast wavelet based image characterization for content based medical image retrieval. In: 2017 International Conference on communication, computing and digital systems (C-CODE). IEEE, pp.351-356, 2017

47.

Deng, L., Yu, D., et al., Deep learning: Methods and applications. Foundations and Trends®, in Signal Processing 7(3–4):197–387, 2014.CrossRef

48.

Premaladha, J., and Ravichandran, K., Novel approaches for diagnosing melanoma skin lesions through supervised and deep learning algorithms. J. Med. Syst. 40(4):96, 2016.PubMedCrossRef

49.

Kharazmi, P., Zheng, J., Lui, H., Wang, Z. J., and Lee, T. K., A computer-aided decision support system for detection and localization of cutaneous vasculature in dermoscopy images via deep feature learning. J. Med. Syst. 42(2):33, 2018.PubMedCrossRef

50.

Wang, S.-H., Phillips, P., Sui, Y., Liu, B., Yang, M., and Cheng, H., Classification of alzheimer’s disease based on eight-layer convolutional neural network with leaky rectified linear unit and max pooling. J. Med. Syst. 42(5):85, 2018.PubMedCrossRef

51.

LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P., Gradient-based learning applied to document recognition. Proc. IEEE 86(11):2278–2324, 1998.CrossRef

52.

LeCun, Y., Bengio, Y., and Hinton, G., Deep learning. Nature 521(7553):436, 2015.CrossRefPubMed

53.

Ding, S., Lin, L., Wang, G., and Chao, H., Deep feature learning with relative distance comparison for person re-identification. Pattern Recog. 48(10):2993–3003, 2015.CrossRef

54.

Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R., Dropout: A simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research 15(1):1929–1958, 2014.

55.

Ioffe, S., and Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv:1502.03167

56.

Kooi, T., Litjens, G., van Ginneken, B., Gubern-Mérida, A., Sánchez, C. I., Mann, R., den Heeten, A., and Karssemeijer, N., Large scale deep learning for computer aided detection of mammographic lesions. Med. Image Anal. 35:303–312, 2017. https://doi.org/10.1016/j.media.2016.07.007. http://www.sciencedirect.com/science/article/pii/S1361841516301244.PubMedCrossRef

57.

Perez, L., and Wang, J.: The effectiveness of data augmentation in image classification using deep learning. arXiv:1712.04621

58.

Hussain, S., Anwar, S. M., and Majid, M., Segmentation of glioma tumors in brain using deep convolutional neural network. Neurocomputing 282:248–261, 2018.CrossRef

59.

Ma, J., Wu, F., Zhu, J., Xu, D., and Kong, D., A pre-trained convolutional neural network based method for thyroid nodule diagnosis. Ultrasonics 73:221–230, 2017.PubMedCrossRef

60.

Sun, W., Tseng, T.-L. B., Zhang, J., and Qian, W., Enhancing deep convolutional neural network scheme for breast cancer diagnosis with unlabeled data. Comput. Med. Imaging Graph. 57:4–9 , 2017.PubMedCrossRef

61.

Pratt, H., Coenen, F., Broadbent, D. M., Harding, S. P., and Zheng, Y., Convolutional neural networks for diabetic retinopathy. Procedia Comput. Sci. 90:200–205, 2016.CrossRef

62.

Anthimopoulos, M., Christodoulidis, S., Ebner, L., Christe, A., and Mougiakakou, S., Lung pattern classification for interstitial lung diseases using a deep convolutional neural network. IEEE Trans. Med. Imaging 35 (5):1207–1216, 2016.PubMedCrossRef

63.

van Tulder, G., and de Bruijne, M., Combining generative and discriminative representation learning for lung ct analysis with convolutional restricted boltzmann machines. IEEE Trans. Med. Imaging 35(5):1262–1272, 2016.PubMedCrossRef

64.

Yan, Z., Zhan, Y., Peng, Z., Liao, S., Shinagawa, Y., Zhang, S., Metaxas, D. N., and Zhou, X. S., Multi-instance deep learning: Discover discriminative local anatomies for bodypart recognition. IEEE Trans. Med. Imaging 35(5):1332–1343, 2016.CrossRef

65.

Sirinukunwattana, K., Raza, S. E. A., Tsang, Y.-W., Snead, D. R., Cree, I. A., and Rajpoot, N. M., Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images. IEEE Trans. Med. Imaging 35(5):1196–1206, 2016.PubMedCrossRef

66.

Qayyum, A., Anwar, S. M., Awais, M., and Majid, M., Medical image retrieval using deep convolutional neural network. Neurocomputing 266:8–20, 2017.CrossRef

67.

Chowdhury, M., Bulo, S. R., Moreno, R., Kundu, M. K., and Smedby, Ö.: An efficient radiographic image retrieval system using convolutional neural network. In: 2016 23rd International Conference on Pattern Recognition (ICPR). IEEE, pp. 3134–3139, 2016

68.

Havaei, M., Davy, A., Warde-Farley, D., Biard, A., Courville, A., Bengio, Y., Pal, C., Jodoin, P.-M., and Larochelle, H., Brain tumor segmentation with deep neural networks. Med. Image Anal. 35:18–31, 2017.PubMedCrossRef

69.

Pereira, S., Pinto, A., Alves, V., and Silva, C. A., Brain tumor segmentation using convolutional neural networks in mri images. IEEE Trans. Med. Imaging 35(5):1240–1251 , 2016.PubMedCrossRef

70.

Jodoin, A. C., Larochelle, H., Pal, C., and Bengio, Y.: Brain tumor segmentation with deep neural networks

71.

Kamnitsas, K., Ledig, C., Newcombe, V. F., Simpson, J. P., Kane, A. D., Menon, D. K., Rueckert, D., and Glocker, B., Efficient multi-scale 3d cnn with fully connected crf for accurate brain lesion segmentation. Med. Image Anal. 36:61–78 , 2017.PubMedCrossRef

72.

Tseng, K.-L., Lin, Y.-L., Hsu, W., and Huang, C.-Y.: Joint sequence learning and cross-modality convolution for 3d biomedical segmentation. arXiv:1704.07754

73.

Casamitjana, A., Puch, S., Aduriz, A., Sayrol, E., and Vilaplana, V.: 3d convolutional networks for brain tumor segmentation. Proceedings of the MICCAI Challenge on Multimodal Brain Tumor Image Segmentation (BRATS), pp. 65–68 , 2016

74.

Farooq, A., Anwar, S., Awais, M., and Rehman, S.: A deep cnn based multi-class classification of alzheimer’s disease using mri. In: 2017 IEEE International Conference on Imaging systems and techniques (IST). IEEE, pp. 1–6, 2017

75.

Farooq, A., Anwar, S., Awais, M., and Alnowami, M.: Artificial intelligence based smart diagnosis of alzheimer’s disease and mild cognitive impairment. In: 2017 International Smart cities conference (ISC2). IEEE, pp. 1–4, 2017

76.

Gangeh, M. J., Sørensen, L., Shaker, S. B., Kamel, M. S., De Bruijne, M., and Loog, M.: A texton-based approach for the classification of lung parenchyma in ct images. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, pp. 595–602, 2010

77.

Sorensen, L., Shaker, S. B., and De Bruijne, M., Quantitative analysis of pulmonary using local binary patterns. IEEE Trans. Med. Imaging 29(2):559–569, 2010.PubMedCrossRef

78.

Anthimopoulos, M., Christodoulidis, S., Christe, A., and Mougiakakou, S.: Classification of interstitial lung disease patterns using local dct features and random forest. In: 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, pp. 6040–6043, 2014

79.

Chen, M., Shi, X., Zhang, Y., Wu, D., and Guizani, M.: Deep features learning for medical image analysis with convolutional autoencoder neural network. IEEE Transactions on Big Data (1) 1–1. https://doi.org/10.1109/TBDATA.2017.2717439, 2017

80.

Hoo-Chang, S., Roth, H. R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D., and Summers, R. M., Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE Trans. Med. Imaging 35(5):1285, 2016.PubMedCentralCrossRef

81.

Simonyan, K., and Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556

82.

Çiçek, Ö., Abdulkadir, A., Lienkamp, S. S., Brox, T., and Ronneberger, O.: 3D u-net Learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M. R., Unal, G., and Wells, W. (Eds.) Medical image computing and computer-assisted intervention – MICCAI, Vol. 2016, pp. 424–432. Springer International Publishing, Cham, 2016.CrossRef

83.

Zhou, Z., Siddiquee, M. M. R., Tajbakhsh, N., and Liang, J.: Unet++: A nested u-net architecture for medical image segmentation. In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support. Springer, pp. 3–11, 2018

84.

Chen, W., Zhang, Y., He, J., Qiao, Y., Chen, Y., Shi, H., and Tang, X.: W-net: Bridged u-net for 2d medical image segmentation. arXiv:1807.04459

85.

Milletari, F., Navab, N., and Ahmadi, S.: V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565–571. https://doi.org/10.1109/3DV.2016.79, 2016

86.

LaLonde, R., and Bagci, U.: Capsules for object segmentation. arXiv:1804.04241

87.

Chen, H., Dou, Q., Yu, L., and Heng, P.-A.: Voxresnet: Deep voxelwise residual networks for volumetric brain segmentation. arXiv:1608.05895

88.

Setio, A. A. A., Ciompi, F., Litjens, G., Gerke, P., Jacobs, C., Van Riel, S. J., Wille, M. M. W., Naqibullah, M., Sánchez, C. I., and van Ginneken, B., Pulmonary nodule detection in ct images: False positive reduction using multi-view convolutional networks. IEEE Trans. Med. Imaging 35(5):1160–1169, 2016.PubMedCrossRef

89.

Brosch, T., Tang, L. Y., Yoo, Y., Li, D. K., Traboulsee, A., and Tam, R., Deep 3d convolutional encoder networks with shortcuts for multiscale feature integration applied to multiple sclerosis lesion segmentation. IEEE Trans. Med. Imaging 35(5):1229–1239, 2016.PubMedCrossRef

90.

Çiçek, Ö., Abdulkadir, A., Lienkamp, S. S., Brox, T., and Ronneberger, O.: 3d u-net: Learning dense volumetric segmentation from sparse annotation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, pp. 424–432, 2016

91.

Ceschin, R., Zahner, A., Reynolds, W., Gaesser, J., Zuccoli, G., Lo, C. W., Gopalakrishnan, V., and Panigrahy, A., A computational framework for the detection of subcortical brain dysmaturation in neonatal mri using 3d convolutional neural networks. NeuroImage 178:183–197, 2018.PubMedCrossRef

92.

Ghafoorian, M., Karssemeijer, N., Heskes, T., Bergkamp, M., Wissink, J., Obels, J., Keizer, K., de Leeuw, F.-E., van Ginneken, B., Marchiori, E., et al., Deep multi-scale location-aware 3d convolutional neural networks for automated detection of lacunes of presumed vascular origin. NeuroImage: Clinical 14:391–399, 2017.CrossRef

93.

Meijs, M., and Manniesing, R.: Artery and vein segmentation of the cerebral vasculature in 4d ct using a 3d fully convolutional neural network. In: Medical Imaging 2018: Computer-Aided Diagnosis, Vol. 10575, International Society for Optics and Photonics, p. 105751Q, 2018

94.

95.

Seong, S.-B., Pae, C., and Park, H.-J., Geometric convolutional neural network for analyzing surface-based neuroimaging data. Frontiers in Neuroinformatics 12:42, 2018.PubMedPubMedCentralCrossRef

96.

Tzeng, E., Hoffman, J., Saenko, K., and Darrell, T.: Adversarial discriminative domain adaptation. In: Computer Vision and Pattern Recognition (CVPR), Vol. 1, p. 4, 2017

Titel: Medical Image Analysis using Convolutional Neural Networks: A Review
verfasst von: Syed Muhammad Anwar
Muhammad Majid
Adnan Qayyum
Muhammad Awais
Majdi Alnowami
Muhammad Khurram Khan
Publikationsdatum: 01.11.2018
Verlag: Springer US
Erschienen in: Journal of Medical Systems / Ausgabe 11/2018
Print ISSN: 0148-5598
Elektronische ISSN: 1573-689X
DOI: https://doi.org/10.1007/s10916-018-1088-1

Springer Medizin

Abstract

Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten

Weitere Artikel der Ausgabe 11/2018

Finding Parameters around the Abdomen for a Vibrotactile System: Healthy and Patients with Parkinson’s Disease

Inter-Patient Modelling of 2D Lung Variations from Chest X-Ray Imaging via Fourier Descriptors

Examining the Influence of E-Health Education on Professional Practice

Research on the Realization of Remote Clinical Skills Training

A Novel Enhanced Gray Scale Adaptive Method for Prediction of Breast Cancer

The Role of PAEHRs in Patient Involvement