박사

신장이식 거부반응과 유방암 전이여부 예측을 위한 합성곱 신경망 기반 병리영상 분석 = A study on digital pathology analysis using convolutional neural network for prediction of renal allograft rejection and breast metastasis

김영곤 2020년
논문상세정보
' 신장이식 거부반응과 유방암 전이여부 예측을 위한 합성곱 신경망 기반 병리영상 분석 = A study on digital pathology analysis using convolutional neural network for prediction of renal allograft rejection and breast metastasis' 의 주제별 논문영향력
논문영향력 선정 방법
논문영향력 요약
주제
  • Deep learning
  • digital pathology
  • image classification
  • metastasis
  • object detection
  • renal allograft
  • semantic segmentation
동일주제 총논문수 논문피인용 총횟수 주제별 논문영향력의 평균
1,574 0

0.0%

' 신장이식 거부반응과 유방암 전이여부 예측을 위한 합성곱 신경망 기반 병리영상 분석 = A study on digital pathology analysis using convolutional neural network for prediction of renal allograft rejection and breast metastasis' 의 참고문헌

  • ¡°Deep neural networks for acoustic modeling in speech recognition
    29 [2012]
  • inception-resnet and the impact of residual connections on learning
  • Yang S-M. A fast method for image noise estimation using laplacian operator and adaptive edge detection
  • Wiener M. Classification and regression by randomForest
    2 ( 3 ) :18-22 . [2002]
  • Weber J. Bayesian belief networks in quantitative histopathology .
    14 ( 6 ) :459-473 . [1992]
  • Vapnik V. Support-vector networks
    20 ( 3 ) :273-297 . [1995]
  • Treanor D. Future-proofing pathology : the case for clinical adoption of digital pathology
    70 ( 12 ) :1010-1018 [2017]
  • Training and validating a deep convolutional neural network for computer-aided detection and classification of abnormalities on frontal chest radiographs
    52 ( 5 ) :281-287 . [2017]
  • Towards building computerized image analysis framework for nucleus discrimination in microscopy images of diffuse glioma
  • The importance of stain normalization in colorectal tissue classification with convolutional networks
    [2017]
  • The human splicing code reveals new insights into the genetic determinants of disease
    347 ( 6218 ) :1254806 . [2015]
  • The elements of statistical learning : data mining , inference and prediction
    27 ( 2 ) :83-85 . [2005]
  • The Banff 2017 Kidney Meeting Report : Revised diagnostic criteria for chronic active T cell–mediated rejection , antibody-mediated rejection , and prospects for integrative endpoints for next-generation clinical trials
    18 ( 2 ) :293-307 . [2018]
  • Texture measures combination for improved meningioma classification of histopathological images
    43 ( 6 ) :2043-2053.71 [2010]
  • Taylor C. Machine learning
    13 [1994]
  • Strategies for training large scale neural network language models
  • Statistical decision-tree models for parsing
  • Solez K. Banff 2003 meeting report : new diagnostic insights and standards .
    4 ( 10 ) :1562-1566 . [2004]
  • Sobel edge detection algorithm
    2 ( 2 ) :1578- 1583 . [2013]
  • Sequence to sequence learning with neural networks
  • Sentinel lymph node biopsy in breastCancer : indications ,Contraindications , andControversies
    41 ( 2 ) :126-133 . [2016]
  • Sentinel lymph node biopsy for patients with early-stage breastCancer : American Society ofClinical OncologyClinical practice guideline update
    32 ( 13 ) :1365-1383 . [2014]
  • Segmentation of magnetic resonance images using aCombination of neural networks and activeContour models
    26 ( 1 ) :71-86 . [2004]
  • Screening mammography withComputer-aided detection : prospective study of 12,860 patients in aCommunity breastCenter
    220 ( 3 ) :781-786 . [2001]
  • Satyanarayanan M. OpenSlide : A vendor-neutral software foundation for digital pathology
    4 [2013]
  • Satya Savithri T. On Segmentation of Nodules from Posterior and AnteriorChest Radiographs
    [2018]
  • Ronquist F. MRBAYES : Bayesian inference of phylogenetic trees .
    17 ( 8 ) :754-755 . [2001]
  • RobustCell nuclei segmentation using statistical modelling
    6 ( 2 ) :79-91 . [1998]
  • Reyes M. A survey of MRI-based medical image analysis for brain tumor studies
    58 ( 13 ) : R97 . [2013]
  • Rethinking the inception architecture forComputer vision
  • Real-time single image and video superresolution using an efficient sub-pixelConvolutional neural network
  • Radiotherapy or surgery of the axilla after a positive sentinel node in breastCancer ( EORTC 1098122023 AMAROS ) : a randomised , multicentre , open-label , phase 3 noninferiority trial
    15 ( 12 ) :1303-1310 . [2014]
  • Question answering with subgraph embeddings
    [2014]
  • Pulmonary nodule detection inCT images : false positive reduction using multi-viewConvolutional networks
    35 ( 5 ) :1160-1169 . [2016]
  • Predicting human brain activity associated with the meanings of nouns .
    320 ( 5880 ) :1191-1195 . [2008]
  • Post-operative arm morbidity and quality of life . Results of the ALMANAC randomised trialComparing sentinel node biopsy with standard axillary treatment in the management of patients with early breastCancer
    95 ( 3 ) :279-293 . [2006]
  • Peritubular capillaritis in renal allografts : prevalence , scoring system , reproducibility and clinicopathological correlates .
    8 ( 4 ) :819-825 . [2008]
  • Pattern recognition and machine learning
    [2006]
  • Patch-based convolutional neural network for whole slide tissue image classification
  • On using very large target vocabulary for neural machine translation
    [2014]
  • Näppi J. CAD in CT colonography without and with oral contrast agents : progress and challenges
    31 ( 4-5 ) :267-284 [2007]
  • Nordholm S. Signal noise reduction by spectral subtraction using linear convolution and casual filtering
    [2001]
  • Nielsen M. Deep feature learning for knee cartilage segmentation using a triplanar convolutional neural network
  • Newman B. Lymphedema after breast cancer : incidence , risk factors , and effect on upper body function
    26 ( 21 ) :3536-3542 . [2008]
  • Multiple-instance learning algorithms for computer-aided detection .
    55 ( 3 ) :1015-1021 . [2008]
  • Montana G. Learning to detect chest radiographs containing pulmonary lesions using visual attention networks .
    53:26-38 [2019]
  • Methods for segmentation and classification of digital microscopy tissue images
    7 [2019]
  • Malik J. Scale-space and edge detection using anisotropic diffusion
    12 ( 7 ) :629-639 . [1990]
  • Maaβ P. Deep learning for tumor classification in imaging mass spectrometry
    34 ( 7 ) :1215-1223 . [2017]
  • Lung nodule classification using deep features in CT images
  • Li L. Discrimination of breast cancer with microcalcifications on mammography by deep learning .
    6:27327 [2016]
  • Leow W-K , Cretu V. Knowledgeguided semantic indexing of breast cancer histopathology images .
  • Least Squares Support Vector Machine Classifiers
    9 ( 3 ) :293-300 . [1999]
  • Learning hierarchical features for scene labeling
    35 ( 8 ) :1915-1929 [2012]
  • Learning deep features for discriminative localization .
  • Large-scale computations on histology images reveal grade-differentiating parameters for breast cancer
    6 ( 1 ) :14 . [2006]
  • Large scale tissue histopathology image classification , segmentation , and visualization via deep convolutional activation features
    18 ( 1 ) :281 . [2017]
  • Large scale deep learning for computer aided detection of mammographic lesions
    35:303-312 [2017]
  • Landgrebe D. A survey of decision tree classifier methodology
    21 ( 3 ) :660-674 . [1991]
  • Kovalev V. Lung image Ssgmentation using deep learning methods and convolutional neural networks
    [2016]
  • K. Deeply-recursive convolutional network for image super-resolution
  • K. Computer-aided diagnostic scheme for the detection of lung nodules on chest radiographs : Localized search method based on anatomical classification .
    33 ( 7Part1 ) :2642-2653 . [2006]
  • Is surgical axillary staging necessary in women with T1 breast cancer who are treated with breast-conserving therapy ?
    39 ( 1 ) :25 . [2019]
  • Influence of computer-aided detection on performance of screening mammography
    356 ( 14 ) :1399-1409 [2007]
  • Index for rating diagnostic tests
    3 ( 1 ) :32-35 . [1950]
  • Improving the accuracy of CTC interpretation : computeraided detection
    20 ( 2 ) :245-257 . [2010]
  • Imagenet classification with deep convolutional neural networks
  • Image texture analysis : methods and comparisons
    72 ( 1 ) :57-71 . [2004]
  • Image analysis for neuroblastoma classification : segmentation of cell nuclei
  • Image analysis and morphometry in the diagnosis of breast cancer
    59 ( 2 ) :109-118 . [2002]
  • Hybrid ICA– Bayesian network approach reveals distinct effective connectivity differences in schizophrenia
    42 ( 4 ) :1560-1568 . [2008]
  • How widely is computer-aided detection used in screening and diagnostic mammography ?
    7 ( 10 ) :802-805 . [2010]
  • Holban S. Segmentation of bone structure in X-ray images using convolutional neural network
    13 ( 1 ) :87-94 . [2013]
  • High-resolution breast cancer screening with multi-view deep convolutional neural networks
    [2017]
  • Harrison 's Principles of Internal Medicine 19th Ed
    [2015]
  • H & E-stained whole slide image deep learning predicts SPOP mutation state in prostate cancer
  • Goldszmidt M. Bayesian network classifiers
    29 ( 2-3 ) :131-163 . [1997]
  • Going deeper with convolutions
  • Frozen-Section Checklist Implementation Improves Quality and Patient Safety
    151 ( 6 ) :607-612 [2019]
  • From detection of individual metastases to classification of lymph node status at the patient level : the CAMELYON17 challenge
    38 ( 2 ) :550-560 [2018]
  • Frisell J. Intraoperative sentinel lymph node examination by frozen section , immunohistochemistry and imprint cytology during breast surgery–a prospective study .
    42 ( 5 ) :617-620 . [2006]
  • Fractional max-pooling
    [2014]
  • Fei-Fei L. Imagenet : A large-scale hierarchical image database
  • Farhadi A. YOLO9000 : better , faster , stronger
  • Factors impacting the accuracy of intra-operative evaluation of sentinel lymph nodes in breast cancer
    24 ( 1 ) :28-34 . [2018]
  • Evolving artificial neural networks
    87 ( 9 ) :1423-1447 . [1999]
  • Evaluating reproducibility of AI algorithms in digital pathology with DAPPER
    15 ( 3 ) : e1006269 . [2019]
  • EfficientNet : Rethinking Model Scaling for Convolutional Neural Networks
    [2019]
  • Effects of chemotherapy on pathologic and biologic characteristics of locally advanced breast cancer
    107 ( 2 ) :211-218 . [1997]
  • Effect of axillary dissection vs no axillary dissection on 10-year overall survival among women with invasive breast cancer and sentinel node metastasis : the ACOSOG Z0011 ( Alliance ) randomized clinical trial
    318 ( 10 ) :918-926 . [2017]
  • Ebrahimi T. The jpeg 2000 still image compression standard
    18 ( 5 ) :36-58 . [2001]
  • Digital pathology evaluation of complement C4d component deposition in the kidney allograft biopsies is a useful tool to improve reproducibility of the scoring .
  • Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer
    318 ( 22 ) :2199-2210 . [2017]
  • Diagnostic accuracy of digital screening mammography with and without computer-aided detection
    175 ( 11 ) :1828-1837 [2015]
  • Development and validation of deep learning–based automatic detection algorithm for malignant pulmonary nodules on chest radiographs
    290 ( 1 ) :218-228 . [2018]
  • Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs
    316 ( 22 ) :2402-2410 . [2016]
  • Detection of prodromal Alzheimer 's disease via pattern classification of magnetic resonance imaging
    29 ( 4 ) :514-523 . [2008]
  • Dermatologist-level classification of skin cancer with deep neural networks
    542 ( 7639 ) :115 . [2017]
  • Denoising functional MR images : a comparison of wavelet denoising and Gaussian smoothing .
    23 ( 3 ) :374-387 . [2004]
  • Deep residual learning for image recognition
  • Deep learning of the tissue-regulated splicing code
    30 ( 12 ) : i121-i129 . [2014]
  • Deep learning for identifying metastatic breast cancer .
    [2016]
  • Deep learning for digital pathology image analysis : A comprehensive tutorial with selected use cases
    7 [2016]
  • Deep learning at chest radiography : automated classification of pulmonary tuberculosis by using convolutional neural networks
    284 ( 2 ) :574-582 . [2017]
  • Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis
    6:26286 [2016]
  • Deep learning algorithms for detection of lymph node metastases from breast cancer : helping artificial intelligence be seen
    318 ( 22 ) :2184-2186 . [2017]
  • Deep convolutional neural networks for LVCSR
  • Costello P. Pulmonary embolism : computer-aided detection at multidetector row spiral computed tomography .
    22 ( 4 ) :319-323 . [2007]
  • Cosmic-ray rejection by Laplacian edge detection
    113 ( 789 ) :1420 . [2001]
  • Computerized image-based detection and grading of lymphocytic infiltration in HER2+ breast cancer histopathology .
    57 ( 3 ) :642-653 . [2009]
  • Computerized analysis of images in the detection and diagnosis of breast cancer
  • Computer-aided diagnosis in radiology : A research plan .
    1 ( 1 ) :72-80 . [1966]
  • Computer-aided detection of lung cancer on chest radiographs : effect on observer performance
    257 ( 2 ) :532-540 . [2010]
  • Computer-aided classification of lung nodules on computed tomography images via deep learning technique
    8 [2015]
  • Comprehensive analysis of lung cancer pathology images to discover tumor shape and boundary features that predict survival outcome
    8 ( 1 ) :10393 . [2018]
  • Classification of breast cancer histology images using convolutional neural networks
    12 ( 6 ) : e0177544 . [2017]
  • Classification and retrieval of digital pathology scans : A new dataset .
  • Carneiro G. Lung segmentation in chest radiographs using distance regularized level set and deep-structured learning and inference
    [2015]
  • Capillary deposition of complement split product C4d in renal allografts is associated with basement membrane injury in peritubular and glomerular capillaries : a contribution of humoral immunity to chronic allograft rejection
    13 ( 9 ) :2371-2380 . [2002]
  • Brox T. U-net : Convolutional networks for biomedical image segmentation
  • Bregler C. Joint training of a convolutional network and a graphical model for human pose estimation
  • Breast image analysis for risk assessment , detection , diagnosis , and treatment of cancer
    15:327-357 [2013]
  • Brain tumor segmentation using convolutional neural networks in MRI images .
    35 ( 5 ) :1240-1251 . [2016]
  • Belongie S. Feature pyramid networks for object detection
  • Banff initiative for quality assurance in transplantation ( BIFQUIT ) : reproducibility of C4d immunohistochemistry in kidney allografts .
    13 ( 5 ) :1235- 1245 . [2013]
  • Axillary dissection versus no axillary dissection in patients with sentinel-node micrometastases ( IBCSG 23–01 ) : a phase 3 randomised controlled trial .
    14 ( 4 ) :297- 305 . [2013]
  • Automatic tuberculosis screening using chest radiographs
    33 ( 2 ) :233-245 . [2013]
  • Automatic segmentation of MR brain images with a convolutional neural network
    35 ( 5 ) :1252-1261 . [2016]
  • Automated mass detection in mammograms using cascaded deep learning and random forests
    [2015]
  • Automated Gleason grading of prostate cancer tissue microarrays via deep learning
    8 [2018]
  • Assessment of convolutional neural networks for automated classification of chest radiographs
    290 ( 2 ) :537-544 . [2018]
  • Ashraf K. Abnormality detection and localization in chest x-rays using deep convolutional neural networks
    [2017]
  • Artificial neural networks : A tutorial
    29 ( 3 ) :31-44 . [1996]
  • A deep convolutional neural network for segmenting and classifying epithelial and stromal regions in histopathological images
    191:214-223 [2016]
  • 56. Otsu N. A threshold selection method from gray-level histograms. IEEE transactions on systems, man, and cybernetics. 1979;9(1):62-66.
  • 20. Thiran J-P, Macq B. Morphological feature extraction for the classification of digital images of cancerous tissues. IEEE Transactions on biomedical engineering. 1996;43(10):1011-1020.
  • 1399 H & E-stained sentinel lymph node sections of breast cancer patients : the CAMELYON dataset .
    7 ( 6 ) : giy065 . [2018]
  • 113. McNutt M. Journals unite for reproducibility. American Association for the Advancement of Science; 2014.