Deep transfer learning-based hologram classification for molecular diagnostics
暂无分享,去创建一个
Cesar M. Castro | Hakho Lee | R. Weissleder | Chuangqi Wang | H. Im | Jouha Min | Kwonmoo Lee | Sung-Jin Kim | Bing Zhao | Nu Ri Choi
[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 .