Virtual UV Fluorescence Microscopy from Hematoxylin and Eosin Staining of Liver Images Using Deep Learning Convolutional Neural Network

[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.