메뉴 건너뛰기




Volumn 6, Issue , 2016, Pages

Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; BREAST; COMPUTER ASSISTED DIAGNOSIS; DIAGNOSTIC IMAGING; ECHOGRAPHY; FEMALE; HUMAN; INFORMATION PROCESSING; LUNG NODULE; MACHINE LEARNING; PATHOLOGY; REPRODUCIBILITY; X-RAY COMPUTED TOMOGRAPHY;

EID: 84964292829     PISSN: None     EISSN: 20452322     Source Type: Journal    
DOI: 10.1038/srep24454     Document Type: Article
Times cited : (670)

References (48)
  • 1
    • 34247171748 scopus 로고    scopus 로고
    • Computer-aided diagnosis in medical imaging: Historical review, current status and future potential
    • Doi, K. Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Comput. Med. Imaging Graph. 31, 198-211 (2007).
    • (2007) Comput. Med. Imaging Graph. , vol.31 , pp. 198-211
    • Doi, K.1
  • 2
    • 81555205692 scopus 로고    scopus 로고
    • Computer-aided diagnosis: How to move from the laboratory to the clinic
    • van Ginneken, B., Schaefer-Prokop, C. M. & Prokop, M. Computer-aided diagnosis: how to move from the laboratory to the clinic. Radiology 261, 719-732 (2011).
    • (2011) Radiology , vol.261 , pp. 719-732
    • Van Ginneken, B.1    Schaefer-Prokop, C.M.2    Prokop, M.3
  • 3
    • 56749181253 scopus 로고    scopus 로고
    • Anniversary paper: History and status of CAD and quantitative image analysis: The role of medical physics and AAPM
    • Giger, M. L., Chan, H.-P. & Boone, J. Anniversary paper: history and status of CAD and quantitative image analysis: the role of medical physics and AAPM. Med. Phys. 35, 5799-5820 (2008).
    • (2008) Med. Phys. , vol.35 , pp. 5799-5820
    • Giger, M.L.1    Chan, H.-P.2    Boone, J.3
  • 4
    • 77952688313 scopus 로고    scopus 로고
    • Computer-aided US diagnosis of breast lesions by using cell-based contour grouping1
    • Cheng, J.-Z. et al. Computer-aided US diagnosis of breast lesions by using cell-based contour grouping1. Radiology 255, 746-754 (2010).
    • (2010) Radiology , vol.255 , pp. 746-754
    • Cheng, J.-Z.1
  • 5
    • 84880524177 scopus 로고    scopus 로고
    • Breast image analysis for risk assessment, detection, diagnosis, and treatment of cancer
    • Giger, M. L., Karssemeijer, N. & Schnabel, J. A. Breast image analysis for risk assessment, detection, diagnosis, and treatment of cancer. Annu. Rev. Biomed. Eng. 15, 327-357 (2013).
    • (2013) Annu. Rev. Biomed. Eng. , vol.15 , pp. 327-357
    • Giger, M.L.1    Karssemeijer, N.2    Schnabel, J.A.3
  • 6
    • 6344221607 scopus 로고    scopus 로고
    • Computer-aided diagnosis of solid breast nodules: Use of an artificial neural network based on multiple sonographic features
    • Joo, S., Yang, Y. S., Moon, W. K. & Kim, H. C. Computer-aided diagnosis of solid breast nodules: use of an artificial neural network based on multiple sonographic features. IEEE Trans. Med. Imag. 23, 1292-1300 (2004).
    • (2004) IEEE Trans. Med. Imag. , vol.23 , pp. 1292-1300
    • Joo, S.1    Yang, Y.S.2    Moon, W.K.3    Kim, H.C.4
  • 7
    • 0037294995 scopus 로고    scopus 로고
    • Breast lesions on sonograms: Computer-aided diagnosis with nearly setting-independent features and artificial neural networks 1
    • Chen, C.-M. et al. Breast Lesions on Sonograms: Computer-aided Diagnosis with Nearly Setting-Independent Features and Artificial Neural Networks 1. Radiology 226, 504-514 (2003).
    • (2003) Radiology , vol.226 , pp. 504-514
    • Chen, C.-M.1
  • 8
    • 58049192571 scopus 로고    scopus 로고
    • Automated method for improving system performance of computer-aided diagnosis in breast ultrasound
    • Drukker, K., Sennett, C. & Giger, M. L. Automated method for improving system performance of computer-aided diagnosis in breast ultrasound. IEEE Trans. Med. Imag. 28, 122-128 (2009).
    • (2009) IEEE Trans. Med. Imag. , vol.28 , pp. 122-128
    • Drukker, K.1    Sennett, C.2    Giger, M.L.3
  • 9
    • 33645120370 scopus 로고    scopus 로고
    • Pulmonary nodules: Estimation of malignancy at thin-section helical CT-effect of computer-aided diagnosis on performance of radiologists 1
    • Awai, K. et al. Pulmonary Nodules: Estimation of Malignancy at Thin-Section Helical CT-Effect of Computer-aided Diagnosis on Performance of Radiologists 1. Radiology 239, 276-284 (2006).
    • (2006) Radiology , vol.239 , pp. 276-284
    • Awai, K.1
  • 10
    • 33646133002 scopus 로고    scopus 로고
    • Distinguishing benign from malignant pulmonary nodules with helical chest CT in children with malignant solid tumors 1
    • McCarville, M. B. et al. Distinguishing Benign from Malignant Pulmonary Nodules with Helical Chest CT in Children with Malignant Solid Tumors 1. Radiology 239, 514-520 (2006).
    • (2006) Radiology , vol.239 , pp. 514-520
    • McCarville, M.B.1
  • 12
    • 84877744350 scopus 로고    scopus 로고
    • Computer-aided diagnosis for early-stage lung cancer based on longitudinal and balanced data
    • Sun, T., Zhang, R., Wang, J., Li, X. & Guo, X. Computer-aided diagnosis for early-stage lung cancer based on longitudinal and balanced data. Plos ONE 8, e63559 (2013).
    • (2013) Plos ONE , vol.8 , pp. e63559
    • Sun, T.1    Zhang, R.2    Wang, J.3    Li, X.4    Guo, X.5
  • 13
    • 67649607477 scopus 로고    scopus 로고
    • Computer-aided diagnosis of pulmonary nodules on CT scans: Improvement of classification performance with nodule surface features
    • Way, T. W. et al. Computer-aided diagnosis of pulmonary nodules on CT scans: improvement of classification performance with nodule surface features. Med. Phys. 36, 3086-3098 (2009).
    • (2009) Med. Phys. , vol.36 , pp. 3086-3098
    • Way, T.W.1
  • 14
    • 4544277237 scopus 로고    scopus 로고
    • Automated lung segmentation for thoracic CT: Impact on computer-aided diagnosis1
    • Armato III, S. G. & Sensakovic, W. F. Automated lung segmentation for thoracic CT: Impact on computer-aided diagnosis1. Acad. Radiol. 11, 1011-1021 (2004).
    • (2004) Acad. Radiol. , vol.11 , pp. 1011-1021
    • Armato, S.G.1    Sensakovic, W.F.2
  • 15
    • 33745644734 scopus 로고    scopus 로고
    • Computer-aided diagnosis of pulmonary nodules on CT scans: Segmentation and classification using 3D active contours
    • Way, T. W. et al. Computer-aided diagnosis of pulmonary nodules on CT scans: segmentation and classification using 3D active contours. Med. Phys. 33, 2323-2337 (2006).
    • (2006) Med. Phys. , vol.33 , pp. 2323-2337
    • Way, T.W.1
  • 16
    • 78650599059 scopus 로고    scopus 로고
    • Computer-aided classification of breast masses: Performance and interobserver variability of expert radiologists versus residents
    • Singh, S., Maxwell, J., Baker, J. A., Nicholas, J. L. & Lo, J. Y. Computer-aided classification of breast masses: Performance and interobserver variability of expert radiologists versus residents. Radiology 258, 73-80 (2011).
    • (2011) Radiology , vol.258 , pp. 73-80
    • Singh, S.1    Maxwell, J.2    Baker, J.A.3    Nicholas, J.L.4    Lo, J.Y.5
  • 17
    • 33847208483 scopus 로고    scopus 로고
    • Malignant and benign breast masses on 3D US volumetric images: Effect of computer-aided diagnosis on radiologist accuracy 1
    • Sahiner, B. et al. Malignant and Benign Breast Masses on 3D US Volumetric Images: Effect of Computer-aided Diagnosis on Radiologist Accuracy 1. Radiology 242, 716-724 (2007).
    • (2007) Radiology , vol.242 , pp. 716-724
    • Sahiner, B.1
  • 18
    • 77952094531 scopus 로고    scopus 로고
    • Selection of diagnostic features on breast MRI to differentiate between malignant and benign lesions using computer-aided diagnosis: Differences in lesions presenting as mass and non-mass-like enhancement
    • Newell, D. et al. Selection of diagnostic features on breast MRI to differentiate between malignant and benign lesions using computer-aided diagnosis: differences in lesions presenting as mass and non-mass-like enhancement. Eur. Radiol. 20, 771-781 (2010).
    • (2010) Eur. Radiol. , vol.20 , pp. 771-781
    • Newell, D.1
  • 19
    • 84890370371 scopus 로고    scopus 로고
    • Robust texture analysis using multi-resolution gray-scale invariant features for breast sonographic tumor diagnosis
    • Yang, M. et al. Robust Texture Analysis Using Multi-Resolution Gray-Scale Invariant Features for Breast Sonographic Tumor Diagnosis. IEEE Trans. Med. Imag. 32, 2262-2273 (2013).
    • (2013) IEEE Trans. Med. Imag. , vol.32 , pp. 2262-2273
    • Yang, M.1
  • 20
    • 84867081344 scopus 로고    scopus 로고
    • Analysis of co-occurrence texture statistics as a function of gray-level quantization for classifying breast ultrasound
    • Gómez, W., Pereira, W. & Infantosi, A. F. C. Analysis of co-occurrence texture statistics as a function of gray-level quantization for classifying breast ultrasound. IEEE Trans. Med. Imag. 31, 1889-1899 (2012).
    • (2012) IEEE Trans. Med. Imag. , vol.31 , pp. 1889-1899
    • Gómez, W.1    Pereira, W.2    Infantosi, A.F.C.3
  • 21
    • 0035661275 scopus 로고    scopus 로고
    • Application of the mutual information criterion for feature selection in computer-aided diagnosis
    • Tourassi, G. D., Frederick, E. D., Markey, M. K. & Floyd Jr, C. E. Application of the mutual information criterion for feature selection in computer-aided diagnosis. Med. Phys. 28, 2394-2402 (2001).
    • (2001) Med. Phys. , vol.28 , pp. 2394-2402
    • Tourassi, G.D.1    Frederick, E.D.2    Markey, M.K.3    Floyd, C.E.4
  • 22
    • 0035544613 scopus 로고    scopus 로고
    • Computer-aided characterization of mammographic masses: Accuracy of mass segmentation and its effects on characterization
    • Sahiner, B. et al. Computer-aided characterization of mammographic masses: accuracy of mass segmentation and its effects on characterization. IEEE Trans. Med. Imag. 20, 1275-1284 (2001).
    • (2001) IEEE Trans. Med. Imag. , vol.20 , pp. 1275-1284
    • Sahiner, B.1
  • 23
    • 78650124769 scopus 로고    scopus 로고
    • ACCOMP: Augmented cell competition algorithm for breast lesion demarcation in sonography
    • Cheng, J.-Z. et al. ACCOMP: augmented cell competition algorithm for breast lesion demarcation in sonography. Med. Phys. 37, 6240-6252 (2010).
    • (2010) Med. Phys. , vol.37 , pp. 6240-6252
    • Cheng, J.-Z.1
  • 24
    • 28844489860 scopus 로고    scopus 로고
    • Cell-competition algorithm: A new segmentation algorithm for multiple objects with irregular boundaries in ultrasound images
    • Chen, C.-M. et al. Cell-competition algorithm: A new segmentation algorithm for multiple objects with irregular boundaries in ultrasound images. Ultrasound Med. Biol. 31, 1647-1664 (2005).
    • (2005) Ultrasound Med. Biol. , vol.31 , pp. 1647-1664
    • Chen, C.-M.1
  • 26
    • 76249103656 scopus 로고    scopus 로고
    • Quantitative analysis of pulmonary emphysema using local binary patterns
    • Sorensen, L., Shaker, S. B. & De Bruijne, M. Quantitative analysis of pulmonary emphysema using local binary patterns. IEEE Trans. Med. Imag. 29, 559-569 (2010).
    • (2010) IEEE Trans. Med. Imag. , vol.29 , pp. 559-569
    • Sorensen, L.1    Shaker, S.B.2    De Bruijne, M.3
  • 27
    • 0032710766 scopus 로고    scopus 로고
    • Journey toward computer-aided diagnosis: Role of image texture analysis 1
    • Tourassi, G. D. Journey toward Computer-aided Diagnosis: Role of Image Texture Analysis 1. Radiology 213, 317-320 (1999).
    • (1999) Radiology , vol.213 , pp. 317-320
    • Tourassi, G.D.1
  • 28
    • 13844267711 scopus 로고    scopus 로고
    • Automatic ultrasound segmentation and morphology based diagnosis of solid breast tumors
    • Chang, R.-F., Wu, W.-J., Moon, W. K. & Chen, D.-R. Automatic ultrasound segmentation and morphology based diagnosis of solid breast tumors. Breast Cancer Res. Treat. 89, 179-185 (2005).
    • (2005) Breast Cancer Res. Treat. , vol.89 , pp. 179-185
    • Chang, R.-F.1    Wu, W.-J.2    Moon, W.K.3    Chen, D.-R.4
  • 29
    • 53149141006 scopus 로고    scopus 로고
    • Computer-aided diagnosis using morphological features for classifying breast lesions on ultrasound
    • Huang, Y. L. et al. Computer-aided diagnosis using morphological features for classifying breast lesions on ultrasound. Ultrasound Obstet. Gynecol. 32, 565-572 (2008).
    • (2008) Ultrasound Obstet. Gynecol. , vol.32 , pp. 565-572
    • Huang, Y.L.1
  • 30
    • 33846561079 scopus 로고    scopus 로고
    • Complexity curve and grey level co-occurrence matrix in the texture evaluation of breast tumor on ultrasound images
    • Alvarenga, A. V., Pereira, W. C., Infantosi, A. F. C. & Azevedo, C. M. Complexity curve and grey level co-occurrence matrix in the texture evaluation of breast tumor on ultrasound images. Med. Phy. 34, 379-387 (2007).
    • (2007) Med. Phy. , vol.34 , pp. 379-387
    • Alvarenga, A.V.1    Pereira, W.C.2    Infantosi, A.F.C.3    Azevedo, C.M.4
  • 31
    • 78449233034 scopus 로고    scopus 로고
    • Segmentation of pulmonary nodules of various densities with morphological approaches and convexity models
    • Kubota, T., Jerebko, A. K., Dewan, M., Salganicoff, M. & Krishnan, A. Segmentation of pulmonary nodules of various densities with morphological approaches and convexity models. Med. Image Anal. 15, 133-154 (2011).
    • (2011) Med. Image Anal. , vol.15 , pp. 133-154
    • Kubota, T.1    Jerebko, A.K.2    Dewan, M.3    Salganicoff, M.4    Krishnan, A.5
  • 32
    • 84923814844 scopus 로고    scopus 로고
    • Latent feature representation with stacked auto-encoder for AD/MCI diagnosis
    • Suk, H.-I., Lee, S.-W. & Shen, D. Latent feature representation with stacked auto-encoder for AD/MCI diagnosis. Brain Struct. Funct. 220, 841-859 (2015).
    • (2015) Brain Struct. Funct. , vol.220 , pp. 841-859
    • Suk, H.-I.1    Lee, S.-W.2    Shen, D.3
  • 33
    • 84921492033 scopus 로고    scopus 로고
    • Deep convolutional neural networks for multi-modality isointense infant brain image segmentation
    • Zhang, W. et al. Deep convolutional neural networks for multi-modality isointense infant brain image segmentation. NeuroImage 108, 214-224 (2015).
    • (2015) NeuroImage , vol.108 , pp. 214-224
    • Zhang, W.1
  • 34
    • 84879853539 scopus 로고    scopus 로고
    • Stacked autoencoders for unsupervised feature learning and multiple organ detection in a pilot study using 4D patient data
    • Shin, H.-C., Orton, M. R., Collins, D. J., Doran, S. J. & Leach, M. O. Stacked autoencoders for unsupervised feature learning and multiple organ detection in a pilot study using 4D patient data. IEEE Trans. Pattern Anal. Mach. Intell. 35, 1930-1943 (2013).
    • (2013) IEEE Trans. Pattern Anal. Mach. Intell. , vol.35 , pp. 1930-1943
    • Shin, H.-C.1    Orton, M.R.2    Collins, D.J.3    Doran, S.J.4    Leach, M.O.5
  • 35
    • 84947424557 scopus 로고    scopus 로고
    • Automatic fetal ultrasound standard plane detection using knowledge transferred recurrent neural networks
    • Chen, H. et al. Automatic Fetal Ultrasound Standard Plane Detection Using Knowledge Transferred Recurrent Neural Networks. Med. Image Comput. Comput. Assist. Interv. (MICCAI) 9349, 507-514 (2015).
    • (2015) Med. Image Comput. Comput. Assist. Interv. (MICCAI) , vol.9349 , pp. 507-514
    • Chen, H.1
  • 36
    • 84969916782 scopus 로고    scopus 로고
    • Improving computer-aided detection using convolutional neural networks and random view aggregation
    • in press
    • Roth, H. et al. Improving Computer-aided Detection using Convolutional Neural Networks and Random View Aggregation. IEEE Trans. Med. Imag. in press, doi:10.1109/tmi.2015.2482920 (2016).
    • (2016) IEEE Trans. Med. Imag.
    • Roth, H.1
  • 37
    • 84951138738 scopus 로고    scopus 로고
    • Leveraging mid-level semantic boundary cues for automated lymph node detection
    • Seff, A. et al. Leveraging Mid-Level Semantic Boundary Cues for Automated Lymph Node Detection. Med. Image Comput. Comput. Assist. Interv. (MICCAI) 9350, 53-61 (2015).
    • (2015) Med. Image Comput. Comput. Assist. Interv. (MICCAI) , vol.9350 , pp. 53-61
    • Seff, A.1
  • 38
    • 84951010232 scopus 로고    scopus 로고
    • Computer-aided pulmonary embolism detection using a novel vessel-aligned multi-planar image representation and convolutional neural networks
    • Tajbakhsh, N., Gotway, M. B. & Liang, J. Computer-aided pulmonary embolism detection using a novel vessel-aligned multi-planar image representation and convolutional neural networks. Med. Image Comput. Comput. Assist. Interv. (MICCAI) 9350, 62-69 (2015).
    • (2015) Med. Image Comput. Comput. Assist. Interv. (MICCAI) , vol.9350 , pp. 62-69
    • Tajbakhsh, N.1    Gotway, M.B.2    Liang, J.3
  • 40
    • 84943752367 scopus 로고    scopus 로고
    • Automatic classification of pulmonary peri-fissural nodules in computed tomography using an ensemble of 2D views and a convolutional neural network out-of - The-box
    • Ciompi, F. et al. Automatic classification of pulmonary peri-fissural nodules in computed tomography using an ensemble of 2D views and a convolutional neural network out-of-the-box. Med. Image Anal. 26, 195-202 (2015).
    • (2015) Med. Image Anal. , vol.26 , pp. 195-202
    • Ciompi, F.1
  • 41
    • 79551480483 scopus 로고    scopus 로고
    • Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
    • Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y. & Manzagol, P.-A. Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 11, 3371-3408 (2010).
    • (2010) J. Mach. Learn. Res. , vol.11 , pp. 3371-3408
    • Vincent, P.1    Larochelle, H.2    Lajoie, I.3    Bengio, Y.4    Manzagol, P.-A.5
  • 43
    • 78649269028 scopus 로고    scopus 로고
    • Distance regularized level set evolution and its application to image segmentation
    • Li, C., Xu, C., Gui, C. & Fox, M. D. Distance regularized level set evolution and its application to image segmentation. IEEE Trans. Imag. Proc. 19, 3243-3254 (2010).
    • (2010) IEEE Trans. Imag. Proc. , vol.19 , pp. 3243-3254
    • Li, C.1    Xu, C.2    Gui, C.3    Fox, M.D.4
  • 44
    • 84871398269 scopus 로고    scopus 로고
    • GrowCut: Interactive multi-label ND image segmentation by cellular automata
    • Vezhnevets, V. & Konouchine, V. GrowCut: Interactive multi-label ND image segmentation by cellular automata. Proc. of Graphicon. 1, 150-156 (2005).
    • (2005) Proc. of Graphicon. , vol.1 , pp. 150-156
    • Vezhnevets, V.1    Konouchine, V.2
  • 45
    • 84908219488 scopus 로고
    • Statistical methods for assessing agreement between two methods of clinical measurement
    • Martin Bland, J. & Altman, D. Statistical methods for assessing agreement between two methods of clinical measurement. The Lancet 327, 307-310 (1986).
    • (1986) The Lancet , vol.327 , pp. 307-310
    • Martin Bland, J.1    Altman, D.2
  • 46
    • 85032751458 scopus 로고    scopus 로고
    • Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups
    • Hinton, G. et al. Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal Process. Mag. 29, 82-97 (2012).
    • (2012) IEEE Signal Process. Mag. , vol.29 , pp. 82-97
    • Hinton, G.1
  • 47
    • 4143056820 scopus 로고    scopus 로고
    • Lung image database consortium: Developing a resource for the medical imaging research community 1
    • Armato III, S. G. et al. Lung image database consortium: Developing a resource for the medical imaging research community 1. Radiology 232, 739-748 (2004).
    • (2004) Radiology , vol.232 , pp. 739-748
    • Armato, S.G.1
  • 48
    • 79551672468 scopus 로고    scopus 로고
    • The lung image database consortium (LIDC) and image database resource initiative (IDRI): A completed reference database of lung nodules on CT scans
    • Armato III, S. G. et al. The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans. Med. Phys. 38, 915-931 (2011).
    • (2011) Med. Phys. , vol.38 , pp. 915-931
    • Armato, S.G.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.