nach oben

International Journal of Multimedia Information Retrieval

Erschienen in:

04.09.2021 | Regular Paper

A review on deep learning in medical image analysis

verfasst von: S. Suganyadevi, V. Seethalakshmi, K. Balasamy

Erschienen in: International Journal of Multimedia Information Retrieval | Ausgabe 1/2022

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Ongoing improvements in AI, particularly concerning deep learning techniques, are assisting to identify, classify, and quantify patterns in clinical images. Deep learning is the quickest developing field in artificial intelligence and is effectively utilized lately in numerous areas, including medication. A brief outline is given on studies carried out on the region of application: neuro, brain, retinal, pneumonic, computerized pathology, bosom, heart, breast, bone, stomach, and musculoskeletal. For information exploration, knowledge deployment, and knowledge-based prediction, deep learning networks can be successfully applied to big data. In the field of medical image processing methods and analysis, fundamental information and state-of-the-art approaches with deep learning are presented in this paper. The primary goals of this paper are to present research on medical image processing as well as to define and implement the key guidelines that are identified and addressed.

Vorheriger Artikel Interactive video retrieval evaluation at a distance: comparing sixteen interactive video search systems in a remote setting at the 10th Video Browser Showdown

Nächster Artikel A fast and robust affine-invariant method for shape registration under partial occlusion

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444. https://doi.org/10.1038/nature14539CrossRef

Razzak MI, Naz ZS, Zaib A (2018) Deep learning for medical image processing: overview, challenges and the future. Classification in BioApps: automation of decision making. Springer, Cham, Switzerland, pp 323–350. https://doi.org/10.1007/978-3-319-65981-7_12

Pang S, Yang X (2016) Deep Convolutional Extreme learning Machine and its application in Handwritten Digit Classification. Hindawi Publ Corp Comput Intell Neurosci 2016:3049632. https://doi.org/10.1155/2016/3049632CrossRef

Chollet F et al (2015) Keras. https://github.com/fchollet

Zhang Y, Zhang S et al (2016) Theano: A Python framework for fast computation of mathematical expressions, arXiv e-prints, abs/1605.02688. http://arxiv.org/abs/1605.02688

Vedaldi A, Lenc K (2015) Matconvnet: convolutional neural networks for matlab. In: Proceedings of the 23rd ACM international conference on Multimedia. ACM, pp 689–692. https://doi.org/10.1145/2733373.2807412

Guo Y, Ashour A (2019) Neutrosophic sets in dermoscopic medical image segmentation. Neutroscophic Set Med Image Anal 11(4):229–243. https://doi.org/10.1016/B978-0-12-818148-5.00011-4

Merjulah R, Chandra J (2019) Classification of myocardial ischemia in delayed contrast enhancement using machine learning. Intell Data Anal Biomed Appl, pp 209–235. https://doi.org/10.1016/B978-0-12-815553-0.00011-2

Oliveira FPM, Tavares JMRS (2014) Medical Image Registration: a review. Comput Methods Biomech Biomed Eng pp 73–93. https://doi.org/10.1080/10255842.2012.670855

10.

Wang J, Zhang M (2020) Deep FLASH: an efficient network for learning-based Medical Image Registration. In: Proceedings of 2020 IEEE/CVF conference on computer vision and pattern recognition (CVPR), pp 4443–4451. https://doi.org/10.1109/cvpr42600.2020.00450

11.

Fu Y, Lei Y, Wang T, Curran WJ, Liu T, Yang X (2020) Deep learning in medical image registration: a review. Phys Med Biol 65(20). https://doi.org/10.1088/1361-6560/ab843e

12.

Haskins G, Kruger U, Yan P (2020) Deep learning in medical image registration: a survey. Mach Vis Appl 31:1–18. https://doi.org/10.1007/s00138-020-01060-xCrossRef

13.

De Vos BD, Wolterink JM, Jong PA, Leiner T, Viergever MA, Isgum I (2017) ConvNet-based localization of anatomical structures in 3D medical images. IEEE Trans Med Imaging 36(7):1470–1481. https://doi.org/10.1109/TMI.2017.2673121CrossRef

14.

Song Y, Cai W, Huang H et al (2013) Region-based progressive localization of cell nuclei in microscopic images with data adaptive modelling. BMC Bioinformatics 14:173. https://doi.org/10.1186/1471-2105-14-173CrossRef

15.

Sharma H, Jain JS, Gupta S, Bansal P (2020) Feature extraction and classification of chest X-ray images using CNN to detect pneumonia. 2020 In: Proceedings of the 10th international conference on cloud computing, data science & engineering (confluence), pp 227–231. https://doi.org/10.1109/Confluence47617.2020.9057809

16.

Hassan M, Ali S, Alquhayz H, Safdar K (2020) Developing intelligent medical image modality classification system using deep transfer learning and LDA. Sci Rep 10(1):12868. https://doi.org/10.1038/s41598-020-69813-2CrossRef

17.

Abbas A, Abdelsamea MM, Gaber MM (2021) Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network. Appl Intell 51:854–864. https://doi.org/10.1007/s10489-020-01829-7

18.

Kowsari K, Sali R, Ehsan L, Adorno W et al (2020) Hierarchical medical image classification, a deep learning approach. Information 11(6):318. https://doi.org/10.3390/info11060318CrossRef

19.

Singh SP, Wang L, Gupta S, Goli H, Padmanabhan P, Gulyas B (2020) 3D deep learning on medical images: a review. Sensors 20(18):5097. https://doi.org/10.3390/s20185097

20.

Shen D, Wu G, Suk H (2017) Deep learning in medical image analysis. Annu Rev Biomed Eng 19:221–248. https://doi.org/10.1146/annurev-bioeng-071516-044442CrossRef

21.

Wang SH, Phillips P, Sui Y, Bin L, Yang M, Cheng H (2018) 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. https://doi.org/10.1007/s10916-018-0932-7CrossRef

22.

Krizhevsky A, Sulskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 60(6):84–90. https://doi.org/10.1145/3065386

23.

Szegedy C, Liu W, Jia Y, Sermanet P, Reed S et al (2015) Going deeper with convolutions. In: Proceedings of the IEEE Computer Society conference on computer vision and pattern recognition; IEEE Computer Society, 7(12), pp 1–9

24.

Lowekamp BC, Chen DT, Ibanez L, Blezek D (2013) The design of SimpleITK. Front Neuroinform 45(7). https://doi.org/10.3389/fninf.2013.00045

25.

Avants B, Tustison N, Song G (2009) Advanced Normalization Tools (ANTS). The Insight Journal. http://hdl.handle.net/10380/3113

26.

Rahmat T, Ismail A, Sharifah A (2019) Chest X-ray image classification using faster R-CNN. Malays J Comput 4(1):225–236. https://doi.org/10.24191/mjoc.v4i1.6095

27.

Jain G, Mittal D, Takur D, Mittal MK (2020) A deep learning approach to detect Covid-19 coronavirus with X-ray images. Elsevier-Biocybern Biomed Eng 40(4):1391–1405. https://doi.org/10.1016/j.bbe.2020.08.008CrossRef

28.

Togacar M, Ergen B, Comert Z (2020) COVID-19 detection using deep learning models to exploit social mimic optimization and structured chest X-ray images using fuzzy color and stacking approaches. Elsevier-Comput Biol Med 121:103805. https://doi.org/10.1016/j.compbiomed.2020.103805

29.

Minaee S, Kafieh R, Sonka M, Yazdani S, Soufi GJ (2020) Deep-COVID: predicting COVID-19 from chest X-ray images using deep transfer learning. Elsevier-Med Image Anal 65:101794. https://doi.org/10.1016/j.media.2020.101794CrossRef

30.

Apostolopoulos ID, Mpesiana TA (2020) Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks. Springer-Phys Eng Sci Med 43(2):635–640. https://doi.org/10.1007/s13246-020-0865-4

31.

Panwar H, Gupta PK, Siddiqui MK, Morales-Menendez R, Singh V (2020) Application of deep learning for fast detection of COVID-19 in X-rays using nCOVnet. Chaos Solitons Fractals 138(3):109944. https://doi.org/10.1016/j.chaos.2020.109944

32.

Ouchicha C, Ammor O, Meknassi M (2020) CVDNet: a novel deep learning architecture for detection of coronavirus (Covid-19) from chest X-ray images. Elsevier-Chaos Solitons Fractals 140(5):110245. https://doi.org/10.1016/j.chaos.2020.110245

33.

Sethy PK, Behera SK, Ratha PK (2020) Detection of coronavirus disease (COVID-19) based on deep features and support vector machine. Int J Math Eng Manag Sci 5(4):643–651. https://doi.org/10.33889/IJMEMS.2020.5.4.052

34.

Jaiswal AK, Tiwari P, Kumar S, Gupta D, Khanna A, Rodrigues JJ (2019) Identifying pneumonia in chest X-rays: a deep learning approach. Measurement 145(2):511–518. https://doi.org/10.1016/j.measurement.2019.05.076CrossRef

35.

Civit-Masot J, Luna-Perejon F, Dominguez Morales M, Civit A (2020) Deep learning system for COVID-19 diagnosis aid using X-ray pulmonary images. Appl Sci 10(13):4640. https://doi.org/10.3390/app10134640CrossRef

36.

Punn NS, Sonbhadra SK, Agarwal S (2020) COVID-19 epidemic analysis using machine learning and deep learning algorithms. https://doi.org/10.1101/2020.04.08.20057679

37.

Dahl GE, Yu D, Deng L, Acero A (2012) Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition. IEEE Trans Audio Speech Lang Process 20(1):30–42. https://doi.org/10.1109/TASL.2011.2134090

38.

Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25(2):1097–1105. https://doi.org/10.1145/3065386CrossRef

39.

Silver D, Huang A, Maddison C et al (2016) Mastering the game of Go with deep neural networks and tree search. Nature 529:484–489. https://doi.org/10.1038/nature16961CrossRef

40.

Mnih V et al (2015) Human-level control through deep reinforcement learning. Nature 518:529–533. https://doi.org/10.1038/nature14236CrossRef

41.

Abràmoff MD, Lou Y, Erginay A, Clarida W, Amelon R, Folk JC, Niemeijer M (2016) Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning. Invest Ophthalmol Vis Sci 57(13):5200–5206. https://doi.org/10.1167/iovs.16-19964CrossRef

42.

Akram SU, Kannala J, Eklund L, Heikkila J (2016) Cell segmentation proposal network for microscopy image analysis. In: Proceedings of the Deep Learning in Medical Image Analysis (DLMIA). Lecture Notes in Computer Science, 10 0 08, pp 21–29. https://doi.org/10.1007/978-3-319-46976-8_3

43.

Ballin A, Karlinsky L, Alpert S, Hasoul S, Ari R, Barkan E (2016) A region based convolutional network for tumor detection and classification in breast mammography. In: Proceedings of the deep learning in medical image analysis (DLMIA). Lecture Notes in Computer Science, 10 0 08, pp 197–205. https://doi.org/10.1007/978-3-319-46976-8_21

44.

Alansary A et al (2016) Fast fully automatic segmentation of the human placenta from motion corrupted MRI. In: Proceedings of the medical image computing and computer-assisted intervention. Lecture Notes in Computer Science, 9901, pp 589–597. https://doi.org/10.1007/978-3-319-46723-8_68

45.

Albarqouni S, Baur C, Achilles F, Belagiannis V, Demirci S, Navab N (2016) Aggnet: deep learning from crowds for mitosis detection in breast cancer histology images. IEEE Trans Med Imaging 35:1313–1321. https://doi.org/10.1109/TMI.2016.2528120CrossRef

46.

Anavi Y, Kogan I, Gelbart E, Geva O, Greenspan H (2015) A comparative study for chest radiograph image retrieval using binary texture and deep learning classification. In: Proceedings of the IEEE engineering in Medicine and Biology Society, pp 2940–2943. https://doi.org/10.1109/EMBC.2015.7319008

47.

Anavi Y, Kogan I, Gelbart E, Geva O, Greenspan H (2016) Visualizing and enhancing a deep learning framework using patients age and gender for chest X-ray IMAGE Retrieval. In: Proceedings of the SPIE on medical imaging, 9785, 978510

48.

Andermatt S, Pezold S, Cattin P (2016) Multi-dimensional gated recurrent units for the segmentation of biomedical 3D-data. In: Proceedings of the deep learning in medical image analysis (DLMIA). Lecture Notes in Computer Science, 10 0 08, pp 142–151

49.

Anthimopoulos M, Christodoulidis S, Ebner L, Christe A, Mougiakakou S (2016) Lung pattern classification for interstitial lung diseases using a deep convolutional neural network. IEEE Trans Med Imaging 35(5):1207–1216. https://doi.org/10.1109/TMI.2016.2535865CrossRef

50.

Antony J, McGuinness K, Connor NEO, Moran K (2016) Quantifying radio graphic knee osteoarthritis severity using deep convolutional neural networks. arxiv: 1609.02469

51.

Apou G, Schaadt NS, Naegel B, Forestier G, Schönmeyer R, Feuerhake F, Wemmert C, Grote A (2016) Detection of lobular structures in normal breast tissue. Comput Biol Med 74:91–102. https://doi.org/10.1016/j.compbiomed.2016.05.004CrossRef

52.

Arevalo J, Gonzalez FA, Pollan R, Oliveira JL, Lopez MAG (2016) Representation learning for mammography mass lesion classification with convolutional neural networks. Comput Methods Programs Biomed 127:248–257. https://doi.org/10.1016/j.cmpb.2015.12.014CrossRef

53.

Baumgartner CF, Kamnitsas K, Matthew J, Smith S, Kainz B, Rueckert D (2016) Real-time standard scan plane detection and localisation in fetal ultrasound using fully convolutional neural networks. In: Proceedings of the medical image computing and computer-assisted intervention. Lecture Notes in Computer Science, 9901, pp 203–211. https://doi.org/10.1007/978-3-319-46723-8_24

54.

Balasamy K, Ramakrishnan S (2019) An intelligent reversible watermarking system for authenticating medical images using wavelet and PSO. Clust Comput 22(2):4431–4442. https://doi.org/10.1007/s10586-018-1991-8CrossRef

55.

Bengio Y (2012) Practical recommendations for gradient-based training of deep architectures. Neural networks: tricks of the trade. Lecture Notes in Computer Science, 7700, Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35289-8_26

56.

Bengio Y, Courville A, Vincent P (2013) Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Match Intell 35(8):1798–1828. https://doi.org/10.1109/TPAMI.2013.50

57.

Bengio Y, Lamblin P, Popovici D, Larochelle H (2007) Greedy layer-wise training of deep networks. In: Proceedings of the advances in neural information processing systems, pp 153–160

58.

Bengio Y, Simard P, Frasconi P (1994) Learning long-term dependencies with gradient descent is difficult. IEEE Trans Neural Networks 5(2):157–166. https://doi.org/10.1109/72.279181CrossRef

59.

Benou A, Veksler R, Friedman A, Raviv TR (2016) Denoising of contrast enhanced MRI sequences by an ensemble of expert deep neural networks. In: Proceedings of the deep learning in medical image analysis (DLMIA). Lecture Notes in Computer Science, 10 0 08, pp 95–110. https://doi.org/10.1146/annurev-bioeng-071516-044442

60.

BenTaieb A, Hamarneh G (2016) Topology aware fully convolutional networks for histology gland segmentation. In: Proceedings of the medical image computing and computer-assisted intervention. Lecture Notes in Computer Science, 9901, pp 460–468. https://doi.org/10.1007/978-3-319-46723-8_53

61.

BenTaieb A, Kawahara J, Hamarneh G (2016) Multi-loss convolutional networks for gland analysis in microscopy. In: Proceedings of the IEEE international symposium on biomedical imaging, pp 642–645. https://doi.org/10.1109/ISBI.2016.7493349

62.

Bergstra J, Bengio Y (2012) Random search for hyper-parameter optimization. J Mach Learn Res 13(10):281–305MathSciNetMATH

63.

Birenbaum A, Greenspan H (2016) Longitudinal multiple sclerosis lesion segmentation using multi-view convolutional neural networks. In: Proceedings of the deep learning in medical image analysis (DLMIA). Lecture Notes in Computer Science, 10 0 08, pp 58–67. https://doi.org/10.1007/978-3-319-46976-8_7

64.

Cheng X, Zhang L, Zheng Y (2015) Deep similarity learning for multimodal medical images. Comput Methods Biomech Biomed Eng pp 248–252. https://doi.org/10.1080/21681163.2015.1135299

65.

Cicero M, Bilbily A, Colak E, Dowdell T, Gray B, Perampaladas K, Barfett J (2017) Training and validating a deep convolutional neural network for computer-aided detection and classification of abnormalities on frontal chest radiographs. Invest Radiol 52(5):281–287. https://doi.org/10.1097/RLI.0000000000000341CrossRef

66.

Ertosun MG, Rubin DL Automated grading of Gliomas using deep learning in digital pathology images: a modular approach with ensemble of convolutional neural networks. In: AMIA annual symposium proceedings, pp 1899–908

67.

Guo Y, Gao Y, Shen D (2016) Deformable MR prostate segmentation via deep feature learning and sparse patch matching. IEEE Trans Med Imaging 35(4):1077–1089. https://doi.org/10.1109/TMI.2015.2508280CrossRef

68.

Guo Y, Wu G, Commander LA, Szary S, Jewells V, Lin W, Shen D (2014) Segmenting Hippocampus from infant brains by sparse patch matching with deep-learned features. In: Proceedings of the medical image computing and computer-assisted intervention. Lecture Notes in Computer Science, 8674, pp 308–315. https://doi.org/10.1007/978-3-319-10470-6_39

69.

Han XH, Lei J, Chen YW (2016) HEp-2 cell classification using K-support spatial pooling in deep CNNs. In: Proceedings of the deep learning in medical image analysis (DLMIA). Lecture Notes in Computer Science, 10 0 08, pp 3–11. https://doi.org/10.1007/978-3-319-46976-8_1

70.

Haugeland J (1985) Artificial intelligence: the very idea. The MIT Press, Cambridge. ISBN: 0262081539

71.

Havaei M, Davy A, Warde Farley D, Biard A, Courville A, Bengio Y, Pal C, Jodoin PM, Larochelle H (2016) Brain tumor segmentation with deep neural networks. Med Image Anal 35:18–31. https://doi.org/10.1016/j.media.2016.05.004CrossRef

72.

Havaei M, Guizard N, Chapados N, Bengio Y (2016) HeMIS: hetero-modal image segmentation. In: Proceedings of the medical image computing and computer-assisted intervention. Lecture Notes in Computer Science, 9901, pp 469–477. https://doi.org/10.1007/978-3-319-46723-8_54

73.

He K, Zhang X, Ren S, Sun J (2015) Deep residual learning for image recognition. arxiv: 1512.03385

74.

Janowczyk A, Madabhushi A (2016) Deep learning for digital pathology image analysis: a comprehensive tutorial with selected use cases. J Pathol Inform 7:29. https://doi.org/10.4103/2153-3539.186902CrossRef

75.

Jia Y, Shelhamer E, Donahue J, Karayev S, Long J, Girshick R, Guadarrama S, Darrell T (2014) Caffe: convolutional architecture for fast feature embedding. In: Proceedings of the twenty-second ACM international conference on multi-media, pp 675–678. https://doi.org/10.1145/2647868.2654.889

76.

Kainz P, Pfeiffer M, Urschler M (2015) Semantic segmentation of colon glands with deep convolutional neural networks and total variation segmentation. arxiv: 1511.06919

77.

Kallen H, Molin J, Heyden A, Lundstr C, Astrom K (2016) Towards grading gleason score using generically trained deep convolutional neural networks. In: Proceedings of the IEEE international symposium on biomedical imaging, pp 1163–1167. https://doi.org/10.1109/ISBI.2016.7493473

78.

Balasamy K, Suganyadevi S (2021) A fuzzy based ROI selection for encryption and watermarking in medical image using DWT and SVD. Multimedia Tools Appl 80:7167–7186. https://doi.org/10.1007/s11042-020-09981-5

79.

Lekadir K, Galimzianova A, Betriu A, Vila MDM, Igual L, Rubin, DL, Fernandez E, Radeva P, Napel S (2017) A convolutional neural network for automatic characterization of plaque composition in carotid ultrasound. IEEE J Biomed Health Inform 21(1):48–55. https://doi.org/10.1109/JBHI.2016.2631401

80.

Li R, Zhang W, Suk HI, Wang L, Li J, Shen D, Ji S (2014) Deep learning based imaging data completion for improved brain disease diagnosis. Med Image Comput Comput-Assist Interv 17(Pt 3):305–312. https://doi.org/10.1007/978-3-319-0443-0_39CrossRef

81.

Li W, Manivannan S, Akbar S, Zhang J, Trucco E, McKenna SJ (2016) Gland segmentation in colon histology images using hand-crafted features and convolutional neural networks. In: Proceedings of the IEEE international symposium on biomedical imaging, pp 1405–1408. https://doi.org/10.1109/ISBI.2016.7493530

82.

Miao S, Wang ZJ, Liao R (2016) A CNN regression approach for real-time 2D/3D registration. IEEE Trans Med Imaging 35(5):1352–1363. https://doi.org/10.1109/TMI.2016.2521800CrossRef

83.

Moeskops P, Viergever MA, Mendrik AM, Vries LSD, Benders MJNL, Isgum I (2016) Automatic segmentation of MR brain images with a convolutional neural network. IEEE Trans Med Imaging 35(5):1252–1262. https://doi.org/10.1109/TMI.2016.2548501CrossRef

84.

Pinaya WHL, Gadelha A, Doyle OM, Noto C, Zugman A, Cordeiro Q, Jackowski AP, Bressan RA, Sato JR (2016) Using deep belief network modelling to characterize differences in brain morphometry in schizophrenia. Nat Sci Rep 6:38897. https://doi.org/10.1038/srep38897CrossRef

85.

Plis SM, Hjelm DR, Salakhutdinov R, Allen EA, Bockholt HJ, Long JD, Johnson HJ, Paulsen JS, Turner JA, Calhoun VD (2014) Deep learning for neuroimaging: a validation study. Front Neurosci 8:229. https://doi.org/10.3389/fnins.2014.00229CrossRef

86.

Poudel RPK, Lamata P, Montana G (2016) Recurrent fully convolutional neural networks for multi-slice MRI cardiac segmentation. arxiv: 1608.03974

87.

Prasoon A, Petersen K, Igel C, Lauze F, Dam E, Nielsen M (2013) Deep feature learning for knee cartilage segmentation using a triplanar convolutional neural network. In: Proceedings of the medical image computing and computer-assisted intervention. Lecture Notes in Computer Science, 8150, pp 246–253. https://doi.org/10.1007/978-3-642-40763-5_31

88.

Rajkomar A, Lingam S, Taylor AG, Blum M, Mongan J (2017) High-throughput classification of radiographs using deep convolutional neural networks. J Digit Imaging 30:95–101. https://doi.org/10.1007/s10278-016-9914-9CrossRef

89.

Ravi D, Wong C, Deligianni F, Berthelot M, Andreu Perez J, Lo B, Yang GZ (2017) Deep learning for health informatics. IEEE J Biomed Health Inform 21(1):4–21. https://doi.org/10.1109/JBHI.2016.2636665CrossRef

90.

Ravishankar H, Prabhu SM, Vaidya V, Singhal N (2016) Hybrid approach for automatic segmentation of fetal abdomen from ultrasound images using deep learning. In: Proceedings of the IEEE international symposium on biomedical imaging, pp 779–782. https://doi.org/10.1109/ISBI.2016.7493382

91.

Sahiner B, Chan HP, Petrick N, Wei D, Helvie MA, Adler DD, Goodsitt MM (1996) Classification of mass and normal breast tissue: a convolution neural network classifier with spatial domain and texture images. IEEE Trans Med Imaging 15(5):598–610. https://doi.org/10.1109/42.538937CrossRef

92.

Samala RK, Chan HP, Hadjiiski LM, Cha K, Helvie MA (2016) Deep-learning convolution neural network for computer-aided detection of microcalcifications in digital breast tomosynthesis. In: Proceedings 9785, medical imaging, computer-aided diagnosis, 97850Y. https://doi.org/10.1117/12.2217092

93.

Samala RK, Chan HP, Hadjiiski L, Helvie MA, Wei J, Cha K (2016) Mass detection in digital breast tomosynthesis: deep convolutional neural network with transfer learning from mammography. Med Phys 43(12):6654. https://doi.org/10.1118/1.4967345.

94.

Sarraf S, Tofighi G (2016) Classification of Alzheimer’s disease using fMRI data and deep learning convolutional neural networks. arxiv: 1603.08631

95.

Schaumberg AJ, Rubin MA, Fuchs TJ (2016) H&E-stained whole slide deep learning predicts SPOP mutation state in prostate cancer. arxiv: 064279. https://doi.org/10.1101/064279

96.

Suganyadevi S, Shamia D, Balasamy K (2021) An IoT-based diet monitoring healthcare system for women. Smart Healthc Syst Des Secur Priv Asp. https://doi.org/10.1002/9781119792253.ch8

97.

Spampinato C, Palazzo S, Giordano D, Aldinucci M, Leonardi R (2016) Deep learning for automated skeletal bone age assessment in X-ray images. Med Image Anal 36:41–51. https://doi.org/10.1016/j.mediaCrossRef

98.

Balasamy K, Shamia D (2021) Feature extraction-based medical image watermarking using fuzzy-based median filter. IETE J Res 1–9. https://doi.org/10.1080/03772063.2021.1893231

99.

Stern D, Payer C, Lepetit V, Urschler M (2016) Automated age estimation from hand MRI volumes using deep learning. In: Proceedings of the medical image computing and computer-assisted intervention. Lecture Notes in Computer Science, 9901, pp 194–202.https://doi.org/10.1007/978-3-319-46723-8_23

100.

Suk HI, Shen D (2013) Deep learning based feature representation for AD/MCI classification. In: Proceedings of the medical image computing and computer-assisted intervention. Lecture Notes in Computer Science, 8150, pp 583–590.https://doi.org/10.1007/978-3-642-40763-5_72

101.

Sun W, Seng B, Zhang J, Qian W (2016) Enhancing deep convolutional neural network scheme for breast cancer diagnosis with unlabeled data. Comput Med Imaging Gr 57:4–9.https://doi.org/10.1016/j.compmedimag

102.

Sun W, Zheng B, Qian W (2016) Computer aided lung cancer diagnosis with deep learning algorithms. In: Proceedings of the SPIE medical imaging, 9785, 97850Z. https://doi.org/10.1117/12.2216307

103.

Teikari P, Santos M, Poon C, Hynynen K (2016) Deep learning convolutional networks for multiphoton microscopy vasculature segmentation. arxiv: 1606.02382

104.

Tran PV (2016) A fully convolutional neural network for cardiac segmentation in short axis MRI. arxiv: 1604.00494.abs/1604.00494

105.

Xie Y, Xing F, Kong X, Su H, Yang L (2015) Beyond classification: structured regression for robust cell detection using convolutional neural network. In: Proceedings of the medical image computing and computer-assisted intervention. Lecture Notes in Computer Science, 9351, pp 358–365. https://doi.org/10.1007/978-3-319-24574-4_43

106.

Xie Y, Zhang Z, Sapkota M, Yang L (2016) Spatial clockwork recurrent neural network for muscle perimysium segmentation. In: Proceedings of the international conference on medical image computing and computer-assisted intervention. Lecture Notes in Computer Science, 9901. Springer, pp 185–193. https://doi.org/10.1007/978-3-319-46723-8_22

107.

Xu T, Zhang H, Huang X, Zhang S, Metaxas DN (2016) Multimodal deep learning for cervical dysplasia diagnosis. In: Proceedings of the medical image computing and computer-assisted intervention. Lecture Notes in Computer Science, 9901, pp 115–123. https://doi.org/10.1007/978-3-319-46723-8_14

108.

Krishnasamy B, Balakrishnan M, Christopher A (2021) A genetic algorithm based medical image watermarking for improving robustness and fidelity in wavelet domain. Intelligent data engineering and analytics. Advances in intelligent systems and computing, 1177. Springer, Singapore. https://doi.org/10.1007/978-981-15-679-1_27

109.

Xu Z, Huang J (2016) Detecting Cells in one second. In: Proceedings of the medical image computing and computer-assisted intervention. Lecture Notes in Computer Science, 9901, pp 676–684. https://doi.org/10.1007/978-3-319-46723-8_78

110.

Yang D, Zhang S, Yan Z, Tan C, Li K, Metaxas D (2015) Automated anatomical landmark detection on distal femur surface using convolutional neural network. In: Proceedings of the IEEE international symposium on biomedical imaging, pp 17–21. https://doi.org/10.1109/isbi.2015.7163806

111.

Yang H, Sun J, Li, H, Wang L, Xu Z (2016) Deep fusion net for multi-atlas segmentation: Application to cardiac MR images. In: Proceedings of the medical image computing and computer assisted intervention. Lecture Notes in Computer Science, 9901, pp 521–528. https://doi.org/10.1007/978-3-319-46723-8_60

112.

Wang S, Yao J, Xu Z, Huang J (2016) Subtype cell detection with an accelerated deep convolution neural network. In: Proceedings of the medical image computing and computer-assisted intervention. In: Lecture Notes in Computer Science, 9901, pp 640–648.https://doi.org/10.1007/978-3-319-46723-8_74

113.

Xu Y, Mo T, Feng Q, Zhong P, Lai M, Chang E (2014) Deep learning of feature representation with multiple instance learning for medical image analysis. In: Proceedings of the IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 1626–1630. https://doi.org/10.1109/ICASSP.2014.6853873

114.

Yang X, Kwitt R, Niethammer M (2016) Fast predictive image registration. In: Proceedings of the deep learning in medical image analysis (DLMIA). Lecture Notes in Computer Science, 10 0 08, pp 48–57. https://doi.org/10.1007/978-3-319-46976-8_6

115.

Yao J, Wang S, Zhu, X, Huang J (2016) Imaging biomarker discovery for lung cancer survival prediction. In: Proceedings of the medical image computing and computer-assisted intervention. Lecture Notes in Computer Science, 9901, pp 649–657. https://doi.org/10.1007/978-3-319-46723-8_75

116.

Zhao J, Zhang M, Zhou Z, Chu J, Cao F (2017) Automatic detection and classification of leukocytes using convolutional neural networks. Med Biol Eng Comput 55(8):1287–1301. https://doi.org/10.1007/s11517-016-1590-x.

117.

Zhang Q, Xiao Y, Dai W, Suo J, Wang C, Shi J, Zheng H (2020) Deep learning based classification of breast tumors with shear-wave elastography. Ultrasonics 72:150–157. https://doi.org/10.1016/j.ultras.2016.08.004CrossRef

118.

Zhang H, Li L, Qiao K, Wang L, Yan B, Li L, Hu G (2016) Image prediction for limited-angle tomography via deep learning with convolutional neural network. arxiv: 1607.08707

119.

Zeiler MD, Fergus R (2014) Visualizing and understanding convolutional networks. In: Proceedings of the European conference on computer vision, pp 818–833

120.

Yu L, Yang X, Chen H, Qin J, Heng P (2017) Volumetric convnets with mixed residual connections for automated prostate segmentation from 3D MR images. In: Proceedings of the thirty-first AAAI conference on artificial intelligence, pp 66–72

Titel: A review on deep learning in medical image analysis
verfasst von: S. Suganyadevi
V. Seethalakshmi
K. Balasamy
Publikationsdatum: 04.09.2021
Verlag: Springer London
Erschienen in: International Journal of Multimedia Information Retrieval / Ausgabe 1/2022
Print ISSN: 2192-6611
Elektronische ISSN: 2192-662X
DOI: https://doi.org/10.1007/s13735-021-00218-1

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Weitere Artikel der Ausgabe 1/2022

Interactive video retrieval evaluation at a distance: comparing sixteen interactive video search systems in a remote setting at the 10th Video Browser Showdown

Multimodal image and audio music transcription

A fast and robust affine-invariant method for shape registration under partial occlusion

Enhancing the performance of 3D auto-correlation gradient features in depth action classification

Premium Partner