Experimental Assessment of Color Deconvolution and Color Normalization for Automated Classification of Histology Images Stained with Hematoxylin and Eosin
暂无分享,去创建一个
Constantino Carlos Reyes-Aldasoro | Francesco Bianconi | Jakob Nikolas Kather | Jakob N Kather | C. Reyes-Aldasoro | F. Bianconi
[1] Andrew Janowczyk,et al. Stain Normalization using Sparse AutoEncoders (StaNoSA): Application to digital pathology , 2017, Comput. Medical Imaging Graph..
[2] Erik Reinhard,et al. Color Transfer between Images , 2001, IEEE Computer Graphics and Applications.
[3] Paolo Napoletano,et al. Hand-Crafted vs Learned Descriptors for Color Texture Classification , 2017, CCIW.
[4] D. Foster. Color constancy , 2011, Vision Research.
[5] J. S. Marron,et al. A method for normalizing histology slides for quantitative analysis , 2009, 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.
[6] Nasir M. Rajpoot,et al. A Nonlinear Mapping Approach to Stain Normalization in Digital Histopathology Images Using Image-Specific Color Deconvolution , 2014, IEEE Transactions on Biomedical Engineering.
[7] Joost van de Weijer,et al. Color in Computer Vision , 2008 .
[8] Geert J. S. Litjens,et al. Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology , 2019, Medical Image Anal..
[9] Jin Tae Kwak,et al. Multiview boosting digital pathology analysis of prostate cancer , 2017, Comput. Methods Programs Biomed..
[10] D. Brat,et al. Predicting cancer outcomes from histology and genomics using convolutional networks , 2017, Proceedings of the National Academy of Sciences.
[11] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[12] Fabrizio Smeraldi,et al. Comparative Evaluation of Hand-Crafted Image Descriptors vs. Off-the-Shelf CNN-Based Features for Colour Texture Classification under Ideal and Realistic Conditions , 2019, Applied Sciences.
[13] Hieu T. Nguyen,et al. Deep Learning Applied for Histological Diagnosis of Breast Cancer , 2020, IEEE Access.
[14] Matti Pietikäinen,et al. Identification of tumor epithelium and stroma in tissue microarrays using texture analysis , 2012, Diagnostic Pathology.
[15] Anant Madabhushi,et al. Statistical shape model for manifold regularization: Gleason grading of prostate histology , 2013, Comput. Vis. Image Underst..
[16] Francesco Bianconi,et al. Collection of textures in colorectal cancer histology , 2016 .
[17] Sidra Nawaz,et al. Mapping spatial heterogeneity in the tumor microenvironment: a new era for digital pathology , 2015, Laboratory Investigation.
[18] Giuseppe De Pietro,et al. A Machine-learning Approach for the Assessment of the Proliferative Compartment of Solid Tumors on Hematoxylin-Eosin-Stained Sections , 2020, Cancers.
[19] Nassir Navab,et al. Structure-Preserving Color Normalization and Sparse Stain Separation for Histological Images , 2016, IEEE Transactions on Medical Imaging.
[20] A. Madabhushi,et al. Artificial intelligence in digital pathology — new tools for diagnosis and precision oncology , 2019, Nature Reviews Clinical Oncology.
[21] Ziqian Wu,et al. A machine learning-based prognostic predictor for stage III colon cancer , 2020, Scientific Reports.
[22] Lasse Riis Østergaard,et al. Exploiting Multiple Color Representations to Improve Colon Cancer Detection in Whole Slide H&E Stains , 2018, COMPAY/OMIA@MICCAI.
[23] Constantino Carlos Reyes-Aldasoro,et al. Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study , 2019, PLoS medicine.
[24] Dmitrii Bychkov,et al. Deep learning based tissue analysis predicts outcome in colorectal cancer , 2018, Scientific Reports.
[25] Andrea Vedaldi,et al. MatConvNet: Convolutional Neural Networks for MATLAB , 2014, ACM Multimedia.
[26] Nasir M. Rajpoot,et al. A Novel Digital Score for Abundance of Tumour Infiltrating Lymphocytes Predicts Disease Free Survival in Oral Squamous Cell Carcinoma , 2019, Scientific Reports.
[27] Konstantinos N. Plataniotis,et al. Circular Mixture Modeling of Color Distribution for Blind Stain Separation in Pathology Images , 2017, IEEE Journal of Biomedical and Health Informatics.
[28] Hao Chen,et al. Gland segmentation in colon histology images: The glas challenge contest , 2016, Medical Image Anal..
[29] Manasi Gyanchandani,et al. Machine Learning Methods for Computer-Aided Breast Cancer Diagnosis Using Histopathology: A Narrative Review. , 2019, Journal of medical imaging and radiation sciences.
[30] Darren Treanor,et al. Digital pathology access and usage in the UK: results from a national survey on behalf of the National Cancer Research Institute’s CM-Path initiative , 2018, Journal of Clinical Pathology.
[31] Stanislav Kovacic,et al. Rotation-invariant texture classification , 2003, Pattern Recognit. Lett..
[32] Peter Hufnagl,et al. Computational morphogenesis - Embryogenesis, cancer research and digital pathology , 2018, Biosyst..
[33] Shyam Lal,et al. A study about color normalization methods for histopathology images. , 2018, Micron.
[34] Francesco Bianconi,et al. Multi-class texture analysis in colorectal cancer histology , 2016, Scientific Reports.
[35] Paolo Napoletano,et al. Improved opponent color local binary patterns: an effective local image descriptor for color texture classification , 2017, J. Electronic Imaging.
[36] Neeraj Kumar,et al. Empirical comparison of color normalization methods for epithelial-stromal classification in H and E images , 2016, Journal of pathology informatics.
[37] Constantino Carlos Reyes-Aldasoro,et al. Evaluation of Colour Pre-processing on Patch-Based Classification of H&E-Stained Images , 2019, ECDP.
[38] Peter H. N. de With,et al. Stain normalization of histopathology images using generative adversarial networks , 2018, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).
[39] Michael J. Swain,et al. Color indexing , 1991, International Journal of Computer Vision.
[40] Paolo Napoletano,et al. Evaluating color texture descriptors under large variations of controlled lighting conditions , 2015, Journal of the Optical Society of America. A, Optics, image science, and vision.
[41] A. Ruifrok,et al. Quantification of histochemical staining by color deconvolution. , 2001, Analytical and quantitative cytology and histology.
[42] Graham D. Finlayson,et al. Colour indexing across devices and viewing conditions , 2001 .
[43] Nasir M. Rajpoot,et al. A Stochastic Polygons Model for Glandular Structures in Colon Histology Images , 2015, IEEE Transactions on Medical Imaging.
[44] Luiz Eduardo Soares de Oliveira,et al. A Dataset for Breast Cancer Histopathological Image Classification , 2016, IEEE Transactions on Biomedical Engineering.
[45] D. Cavouras,et al. Computer-based association of the texture of expressed estrogen receptor nuclei with histologic grade using immunohistochemically-stained breast carcinomas. , 2009, Analytical and quantitative cytology and histology.
[46] Nassir Navab,et al. Staingan: Stain Style Transfer for Digital Histological Images , 2018, 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019).
[47] Begonya Garcia-Zapirain,et al. Breast Cancer Histopathology Image Classification Using an Ensemble of Deep Learning Models , 2020, Sensors.
[48] Arkadiusz Gertych,et al. Machine learning approaches to analyze histological images of tissues from radical prostatectomies , 2015, Comput. Medical Imaging Graph..
[49] Francesco Bianconi,et al. Automatic Characterization of the Visual Appearance of Industrial Materials through Colour and Texture Analysis: An Overview of Methods and Applications , 2013 .
[50] Francesco Bianconi,et al. Rotation invariant co-occurrence features based on digital circles and discrete Fourier transform , 2014, Pattern Recognit. Lett..
[51] Matti Pietikäinen,et al. Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..
[52] Robert M. Haralick,et al. Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..
[53] A. Madabhushi. Digital pathology image analysis: opportunities and challenges. , 2009, Imaging in medicine.
[54] Andrew H. Beck,et al. Systematic Analysis of Breast Cancer Morphology Uncovers Stromal Features Associated with Survival , 2011, Science Translational Medicine.
[55] Jens Rittscher,et al. The use of digital pathology and image analysis in clinical trials , 2019, The journal of pathology. Clinical research.
[56] Nikos Grammalidis,et al. Grading of invasive breast carcinoma through Grassmannian VLAD encoding , 2017, PloS one.
[57] T. Meckel,et al. A Model based Survey of Colour Deconvolution in Diagnostic Brightfield Microscopy: Error Estimation and Spectral Consideration , 2015, Scientific Reports.
[58] Andrew Zisserman,et al. Return of the Devil in the Details: Delving Deep into Convolutional Nets , 2014, BMVC.
[59] J. Slodkowska,et al. Digital pathology in personalized cancer therapy. , 2012, Folia histochemica et cytobiologica.
[60] Xuejie Zhang,et al. Parallel Structure Deep Neural Network Using CNN and RNN with an Attention Mechanism for Breast Cancer Histology Image Classification , 2019, Cancers.
[61] Bram van Ginneken,et al. The importance of stain normalization in colorectal tissue classification with convolutional networks , 2017, 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017).
[62] Matti Pietikäinen,et al. Accurate color discrimination with classification based on feature distributions , 1996, Proceedings of 13th International Conference on Pattern Recognition.
[63] Michael Gadermayr,et al. A Quantitative Assessment of Image Normalization for Classifying Histopathological Tissue of the Kidney , 2017, GCPR.
[64] Manuel Fernández Delgado,et al. Influence of normalization and color space to color texture classification , 2017, Pattern Recognit..
[65] Marco Novelli,et al. Deep learning for prediction of colorectal cancer outcome: a discovery and validation study , 2020, The Lancet.
[66] Luiz Eduardo Soares de Oliveira,et al. Multiple instance learning for histopathological breast cancer image classification , 2019, Expert Syst. Appl..
[67] Sergey Ioffe,et al. Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[68] Daisuke Komura,et al. Machine Learning Methods for Histopathological Image Analysis , 2017, Computational and structural biotechnology journal.
[69] Lior Shamir,et al. IICBU 2008: a proposed benchmark suite for biological image analysis , 2008, Medical & Biological Engineering & Computing.
[70] Jon Griffin,et al. Digital pathology in clinical use: where are we now and what is holding us back? , 2017, Histopathology.
[71] Darren Treanor,et al. Colour in digital pathology: a review , 2017, Histopathology.