Deep transfer learning-based hologram classification for molecular diagnostics

[1]  Andrew H. Beck,et al.  Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer , 2017, JAMA.

[2]  Terry Taewoong Um,et al.  Multi-categorical deep learning neural network to classify retinal images: A pilot study employing small database , 2017, PloS one.

[3]  Van Lam,et al.  Automatic phase aberration compensation for digital holographic microscopy based on deep learning background detection. , 2017, Optics express.

[4]  Heung-Il Suk,et al.  Deep Learning in Medical Image Analysis. , 2017, Annual review of biomedical engineering.

[5]  Yibo Zhang,et al.  Phase recovery and holographic image reconstruction using deep learning in neural networks , 2017, Light: Science & Applications.

[6]  Sebastian Thrun,et al.  Dermatologist-level classification of skin cancer with deep neural networks , 2017, Nature.

[7]  Subhashini Venugopalan,et al.  Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. , 2016, JAMA.

[8]  Cesar M. Castro,et al.  Holographic Assessment of Lymphoma Tissue (HALT) for Global Oncology Field Applications , 2016, Theranostics.

[9]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[10]  Hakho Lee,et al.  Digital diffraction analysis enables low-cost molecular diagnostics on a smartphone , 2015, Proceedings of the National Academy of Sciences.

[11]  Yoshua Bengio,et al.  How transferable are features in deep neural networks? , 2014, NIPS.

[12]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[13]  Juan Carlos Fernández,et al.  Multiobjective evolutionary algorithms to identify highly autocorrelated areas: the case of spatial distribution in financially compromised farms , 2014, Ann. Oper. Res..

[14]  Ivan Laptev,et al.  Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[15]  Stefan Carlsson,et al.  CNN Features Off-the-Shelf: An Astounding Baseline for Recognition , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops.

[16]  Rob Fergus,et al.  Visualizing and Understanding Convolutional Networks , 2013, ECCV.

[17]  Trevor Darrell,et al.  DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition , 2013, ICML.

[18]  Aydogan Ozcan,et al.  Increased space-bandwidth product in pixel super-resolved lensfree on-chip microscopy , 2013, Scientific Reports.

[19]  Hongying Zhu,et al.  Optical imaging techniques for point-of-care diagnostics. , 2013, Lab on a chip.

[20]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[21]  Aydogan Ozcan,et al.  Imaging without lenses: achievements and remaining challenges of wide-field on-chip microscopy , 2012, Nature Methods.

[22]  David M. Kaz,et al.  Measuring translational, rotational, and vibrational dynamics in colloids with digital holographic microscopy. , 2011, Optics express.

[23]  Feng Xu,et al.  Miniaturized lensless imaging systems for cell and microorganism visualization in point‐of‐care testing , 2011, Biotechnology journal.

[24]  Derek K. Tseng,et al.  Detection of waterborne parasites using field-portable and cost-effective lensfree microscopy. , 2010, Lab on a chip.

[25]  Derek Tseng,et al.  Compact, light-weight and cost-effective microscope based on lensless incoherent holography for telemedicine applications. , 2010, Lab on a chip.

[26]  Bo Sun,et al.  Flow visualization and flow cytometry with holographic video microscopy. , 2009 .

[27]  Fei-Fei Li,et al.  ImageNet: A large-scale hierarchical image database , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[28]  Constantin F. Aliferis,et al.  A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification , 2008, BMC Bioinformatics.

[29]  T. Latychevskaia,et al.  Solution to the twin image problem in holography. , 2006, Physical review letters.

[30]  Peter Klages,et al.  Digital in-line holographic microscopy. , 2006, Applied optics.

[31]  Fujio Shimizu,et al.  Fresnel diffraction mirror for an atomic wave. , 2005, Physical review letters.

[32]  W Xu,et al.  Digital in-line holography for biological applications , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[33]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[34]  Lorien Y. Pratt,et al.  Discriminability-Based Transfer between Neural Networks , 1992, NIPS.

[35]  J R Fienup,et al.  Phase retrieval algorithms: a comparison. , 1982, Applied optics.

[36]  J R Fienup,et al.  Reconstruction of an object from the modulus of its Fourier transform. , 1978, Optics letters.

[37]  M. Stone Cross‐Validatory Choice and Assessment of Statistical Predictions , 1976 .

[38]  Jacob Cohen A Coefficient of Agreement for Nominal Scales , 1960 .

[39]  Patricia M. Cisarik,et al.  A Comparison , 1913, Texas medical journal.

[40]  Karl Pearson F.R.S. LIII. On lines and planes of closest fit to systems of points in space , 1901 .

[41]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[42]  Jia Deng,et al.  A large-scale hierarchical image database , 2009, CVPR 2009.

[43]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[44]  Bernhard Schölkopf,et al.  A Primer on Kernel Methods , 2004 .

[45]  Vikas Sindhwani,et al.  Information Theoretic Feature Crediting in Multiclass Support Vector Machines , 2001, SDM.

[46]  Lawrence D. Jackel,et al.  Handwritten Digit Recognition with a Back-Propagation Network , 1989, NIPS.

[47]  R. Gerchberg A practical algorithm for the determination of phase from image and diffraction plane pictures , 1972 .