Comparison of Deep Transfer Learning Strategies for Digital Pathology
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[1] Bram van Ginneken,et al. A survey on deep learning in medical image analysis , 2017, Medical Image Anal..
[2] Ronald M. Summers,et al. Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning , 2016, IEEE Transactions on Medical Imaging.
[3] Yolanda T. Chong,et al. Automated analysis of high‐content microscopy data with deep learning , 2017, Molecular systems biology.
[4] Hamid R. Tizhoosh,et al. A comparative study of CNN, BoVW and LBP for classification of histopathological images , 2017, 2017 IEEE Symposium Series on Computational Intelligence (SSCI).
[5] Bram van Ginneken,et al. Off-the-shelf convolutional neural network features for pulmonary nodule detection in computed tomography scans , 2015, 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI).
[6] Chih-Jen Lin,et al. LIBLINEAR: A Library for Large Linear Classification , 2008, J. Mach. Learn. Res..
[7] Pierre Geurts,et al. Extremely randomized trees , 2006, Machine Learning.
[8] Xiang Zhang,et al. OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks , 2013, ICLR.
[9] Hayit Greenspan,et al. Chest pathology detection using deep learning with non-medical training , 2015, 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI).
[10] Raphaël Marée,et al. The Need for Careful Data Collection for Pattern Recognition in Digital Pathology , 2017, Journal of pathology informatics.
[11] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[12] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..
[13] Yuanjie Zheng,et al. Breast Cancer Multi-classification from Histopathological Images with Structured Deep Learning Model , 2017, Scientific Reports.
[14] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[15] Sergey Ioffe,et al. Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[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] Janne Heikkilä,et al. Transfer Learning for Cell Nuclei Classification in Histopathology Images , 2016, ECCV Workshops.
[19] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[20] Joel H. Saltz,et al. Automatic histopathology image analysis with CNNs , 2016, 2016 New York Scientific Data Summit (NYSDS).
[21] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[22] 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.
[23] Bram van Ginneken,et al. Automatic classification of pulmonary peri-fissural nodules in computed tomography using an ensemble of 2D views and a convolutional neural network out-of-the-box , 2015, Medical Image Anal..
[24] Hamid R. Tizhoosh,et al. Convolutional neural networks for histopathology image classification: Training vs. Using pre-trained networks , 2017, 2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA).
[25] Sebastian Thrun,et al. Dermatologist-level classification of skin cancer with deep neural networks , 2017, Nature.
[26] Bo Chen,et al. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications , 2017, ArXiv.
[27] Gilles Louppe,et al. Collaborative analysis of multi-gigapixel imaging data using Cytomine , 2016, Bioinform..
[28] Raphaël Marée,et al. Towards generic image classification using tree-based learning: An extensive empirical study , 2016, Pattern Recognit. Lett..
[29] Jean-Christophe Olivo-Marin,et al. An approach for detection of glomeruli in multisite digital pathology , 2016, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI).
[30] Fei-Fei Li,et al. ImageNet: A large-scale hierarchical image database , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.
[31] Hariharan Ravishankar,et al. Understanding the Mechanisms of Deep Transfer Learning for Medical Images , 2016, LABELS/DLMIA@MICCAI.
[32] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[33] Qiang Yang,et al. A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.
[34] Nima Tajbakhsh,et al. Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning? , 2016, IEEE Transactions on Medical Imaging.
[35] Subhashini Venugopalan,et al. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. , 2016, JAMA.
[36] Yoshua Bengio,et al. How transferable are features in deep neural networks? , 2014, NIPS.
[37] Bahram Parvin,et al. Automated Histology Analysis: Opportunities for signal processing , 2015, IEEE Signal Processing Magazine.
[38] Edward Kim,et al. A deep semantic mobile application for thyroid cytopathology , 2016, SPIE Medical Imaging.
[39] Ronald M. Summers,et al. Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique , 2016 .
[40] Kilian Q. Weinberger,et al. Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[41] Quoc V. Le,et al. Do Better ImageNet Models Transfer Better? , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[42] Sergey Ioffe,et al. Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning , 2016, AAAI.
[43] Jason Weston,et al. Gene Selection for Cancer Classification using Support Vector Machines , 2002, Machine Learning.
[44] Hongfei Lin,et al. Deep Transfer Learning for Modality Classification of Medical Images , 2017, Inf..
[45] Joseph Antony,et al. Quantifying radiographic knee osteoarthritis severity using deep convolutional neural networks , 2016, 2016 23rd International Conference on Pattern Recognition (ICPR).