Virtual UV Fluorescence Microscopy from Hematoxylin and Eosin Staining of Liver Images Using Deep Learning Convolutional Neural Network
暂无分享,去创建一个
Przemysław Mazurek | Dorota Oszutowska-Mazurek | Miroslaw Parafiniuk | P. Mazurek | M. Parafiniuk | Dorota Oszutowska-Mazurek
[1] H. Robbins. A Stochastic Approximation Method , 1951 .
[2] Junko Ota,et al. Application of Super-Resolution Convolutional Neural Network for Enhancing Image Resolution in Chest CT , 2018, Journal of Digital Imaging.
[3] Joakim Lindblad,et al. Blind Color Decomposition of Histological Images , 2013, IEEE Transactions on Medical Imaging.
[4] Patrick Granton,et al. Radiomics: extracting more information from medical images using advanced feature analysis. , 2012, European journal of cancer.
[5] Krzysztof Okarma,et al. Current Trends and Advances in Image Quality Assessment , 2019, Elektronika ir Elektrotechnika.
[6] Kunio Doi,et al. Computer-aided diagnosis in medical imaging: Historical review, current status and future potential , 2007, Comput. Medical Imaging Graph..
[7] Guangtao Zhai,et al. Perceptual image quality assessment: a survey , 2020, Science China Information Sciences.
[8] Jakob Nikolas Kather,et al. New Colors for Histology: Optimized Bivariate Color Maps Increase Perceptual Contrast in Histological Images , 2015, PloS one.
[9] A. Ozcan,et al. Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning , 2018, Nature Biomedical Engineering.
[10] Zunlei Feng,et al. Neural Style Transfer: A Review , 2017, IEEE Transactions on Visualization and Computer Graphics.
[11] Eero P. Simoncelli,et al. Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.
[12] Lopamudra Mukherjee,et al. Super-resolution recurrent convolutional neural networks for learning with multi-resolution whole slide images , 2019, Journal of biomedical optics.
[13] B. N. Chatterji,et al. An FFT-based technique for translation, rotation, and scale-invariant image registration , 1996, IEEE Trans. Image Process..
[14] Apostolos Delis,et al. Digital synthesis of histological stains using micro-structured and multiplexed virtual staining of label-free tissue , 2020, Light, science & applications.
[15] M. Titford. Progress in the Development of Microscopical Techniques for Diagnostic Pathology , 2009 .
[16] Wolfgang Heidrich,et al. Unsharp Masking, Countershading and Halos: Enhancements or Artifacts? , 2012, Comput. Graph. Forum.
[17] Fei Gao,et al. Objective image quality assessment: a survey , 2014, Int. J. Comput. Math..
[18] R. Henriques,et al. Between life and death: strategies to reduce phototoxicity in super-resolution microscopy , 2020, Journal of physics D: Applied physics.
[19] C. Rueden,et al. Metadata matters: access to image data in the real world , 2010, The Journal of cell biology.
[20] Geoffrey E. Hinton,et al. Deep Learning , 2015, Nature.
[21] Jie Tian,et al. Deep Learning for Virtual Histological Staining of Bright-Field Microscopic Images of Unlabeled Carotid Artery Tissue , 2020, Molecular Imaging and Biology.
[22] Andrea Montanari,et al. A mean field view of the landscape of two-layer neural networks , 2018, Proceedings of the National Academy of Sciences.
[23] Trevor Darrell,et al. Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[24] Andre Dekker,et al. Radiomics: the process and the challenges. , 2012, Magnetic resonance imaging.
[25] Dirk P. Kroese,et al. Why the Monte Carlo method is so important today , 2014 .
[26] G. Drummen,et al. Advanced Fluorescence Microscopy Techniques—FRAP, FLIP, FLAP, FRET and FLIM , 2012, Molecules.