Prospective identification of hematopoietic lineage choice by deep learning

[1]  Stavroula Skylaki,et al.  Challenges in long-term imaging and quantification of single-cell dynamics , 2016, Nature Biotechnology.

[2]  Carsten Marr,et al.  Early myeloid lineage choice is not initiated by random PU.1 to GATA1 protein ratios , 2016, Nature.

[3]  Carsten Marr,et al.  Software tools for single-cell tracking and quantification of cellular and molecular properties , 2016, Nature Biotechnology.

[4]  John Salvatier,et al.  Theano: A Python framework for fast computation of mathematical expressions , 2016, ArXiv.

[5]  Fabian J. Theis,et al.  Network plasticity of pluripotency transcription factors in embryonic stem cells , 2015, Nature Cell Biology.

[6]  Mark R. Winter,et al.  Computational Image Analysis Reveals Intrinsic Multigenerational Differences between Anterior and Posterior Cerebral Cortex Neural Progenitor Cells , 2015, Stem Cell Reports.

[7]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[8]  Luca Maria Gambardella,et al.  Assessment of algorithms for mitosis detection in breast cancer histopathology images , 2014, Medical Image Anal..

[9]  Philipp S. Hoppe,et al.  Single-cell technologies sharpen up mammalian stem cell research , 2014, Nature Cell Biology.

[10]  Trevor Darrell,et al.  Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.

[11]  Sven Behnke,et al.  PyStruct: learning structured prediction in python , 2014, J. Mach. Learn. Res..

[12]  R. Sandberg.  Entering the era of single-cell transcriptomics in biology and medicine , 2013, Nature Methods.

[13]  Navdeep Jaitly,et al.  Hybrid speech recognition with Deep Bidirectional LSTM , 2013, 2013 IEEE Workshop on Automatic Speech Recognition and Understanding.

[14]  Fabian J Theis,et al.  An automatic method for robust and fast cell detection in bright field images from high-throughput microscopy , 2013, BMC Bioinformatics.

[15]  Slobodan Vucetic,et al.  BudgetedSVM: a toolbox for scalable SVM approximations , 2013, J. Mach. Learn. Res..

[16]  Takeo Kanade,et al.  A Semi-Markov Model for Mitosis Segmentation in Time-Lapse Phase Contrast Microscopy Image Sequences of Stem Cell Populations , 2012, IEEE Transactions on Medical Imaging.

[17]  Luca Maria Gambardella,et al.  Flexible, High Performance Convolutional Neural Networks for Image Classification , 2011, IJCAI.

[18]  Timm Schroeder,et al.  Long-term single-cell imaging of mammalian stem cells , 2011, Nature Methods.

[19]  Takeo Kanade,et al.  Automated Mitosis Detection of Stem Cell Populations in Phase-Contrast Microscopy Images , 2011, IEEE Transactions on Medical Imaging.

[20]  Andrea Vedaldi,et al.  Vlfeat: an open and portable library of computer vision algorithms , 2010, ACM Multimedia.

[21]  Geoffrey E. Hinton,et al.  Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.

[22]  Takeo Kanade,et al.  Mitosis sequence detection using hidden conditional random fields , 2010, 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[23]  Yoshua Bengio,et al.  Understanding the difficulty of training deep feedforward neural networks , 2010, AISTATS.

[24]  Andrew R. Cohen,et al.  Computational prediction of neural progenitor cell fates , 2010, Nature Methods.

[25]  Urbano Nunes,et al.  Trainable classifier-fusion schemes: An application to pedestrian detection , 2009, 2009 12th International IEEE Conference on Intelligent Transportation Systems.

[26]  Pekka Ruusuvuori,et al.  Bright Field Microscopy as an Alternative to Whole Cell Fluorescence in Automated Analysis of Macrophage Images , 2009, PloS one.

[27]  Vincent Lepetit,et al.  Fast Ray features for learning irregular shapes , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[28]  Philipp S. Hoppe,et al.  Hematopoietic Cytokines Can Instruct Lineage Choice , 2009, Science.

[29]  H. Blau,et al.  Perturbation of single hematopoietic stem cell fates in artificial niches. , 2009, Integrative biology : quantitative biosciences from nano to macro.

[30]  Timm Schroeder,et al.  Exploring Hematopoiesis at Single Cell Resolution , 2008, Cells Tissues Organs.

[31]  Marc'Aurelio Ranzato,et al.  Unsupervised Learning of Invariant Feature Hierarchies with Applications to Object Recognition , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[32]  Eric Jervis,et al.  High-resolution video monitoring of hematopoietic stem cells cultured in single-cell arrays identifies new features of self-renewal. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[33]  S. Morrison,et al.  Supplemental Experimental Procedures , 2022 .

[34]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[35]  Jürgen Schmidhuber,et al.  Framewise phoneme classification with bidirectional LSTM and other neural network architectures , 2005, Neural Networks.

[36]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[37]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[38]  Hiromitsu Nakauchi,et al.  Long-Term Lymphohematopoietic Reconstitution by a Single CD34-Low/Negative Hematopoietic Stem Cell , 1996, Science.

[39]  Yoshua Bengio,et al.  Learning long-term dependencies with gradient descent is difficult , 1994, IEEE Trans. Neural Networks.

[40]  Martin A. Riedmiller,et al.  RPROP - A Fast Adaptive Learning Algorithm , 1992 .

[41]  Hideyuki Tamura,et al.  Textural Features Corresponding to Visual Perception , 1978, IEEE Transactions on Systems, Man, and Cybernetics.

[42]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[43]  D. Gabor,et al.  Theory of communication. Part 1: The analysis of information , 1946 .

[44]  von F. Zernike.  Beugungstheorie des schneidenver-fahrens und seiner verbesserten form, der phasenkontrastmethode , 1934 .