Active, continual fine tuning of convolutional neural networks for reducing annotation efforts

[1]  C. E. SHANNON,et al.  A mathematical theory of communication , 1948, MOCO.

[2]  Michael McCloskey,et al.  Catastrophic Interference in Connectionist Networks: The Sequential Learning Problem , 1989 .

[3]  R. Tibshirani,et al.  An introduction to the bootstrap , 1993 .

[4]  Shlomo Argamon,et al.  Committee-Based Sampling For Training Probabilistic Classi(cid:12)ers , 1995 .

[5]  Andrew McCallum,et al.  Employing EM and Pool-Based Active Learning for Text Classification , 1998, ICML.

[6]  William M. Wells,et al.  Medical Image Computing and Computer-Assisted Intervention — MICCAI’98 , 1998, Lecture Notes in Computer Science.

[7]  Stefan Wrobel,et al.  Active Hidden Markov Models for Information Extraction , 2001, IDA.

[8]  Nathalie Japkowicz,et al.  The class imbalance problem: A systematic study , 2002, Intell. Data Anal..

[9]  Matjaz Kukar,et al.  Transductive reliability estimation for medical diagnosis , 2003, Artif. Intell. Medicine.

[10]  Andrew McCallum,et al.  Reducing Labeling Effort for Structured Prediction Tasks , 2005, AAAI.

[11]  Maria-Florina Balcan,et al.  Margin Based Active Learning , 2007, COLT.

[12]  Pietro Perona,et al.  Entropy-based active learning for object recognition , 2008, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[13]  Li Fei-Fei,et al.  ImageNet: A large-scale hierarchical image database , 2009, CVPR.

[14]  Haibo He,et al.  Learning from Imbalanced Data , 2009, IEEE Transactions on Knowledge and Data Engineering.

[15]  Isabelle Guyon,et al.  Results of the Active Learning Challenge , 2011, Active Learning and Experimental Design @ AISTATS.

[16]  George C. Runger,et al.  Active Batch Learning with Stochastic Query-by-Forest (SQBF) , 2011 .

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

[18]  Xiao-Tong Yuan,et al.  Truncated power method for sparse eigenvalue problems , 2011, J. Mach. Learn. Res..

[19]  Xin Li,et al.  Adaptive Active Learning for Image Classification , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[20]  Nima Tajbakhsh,et al.  Computer-Aided Pulmonary Embolism Detection Using a Novel Vessel-Aligned Multi-planar Image Representation and Convolutional Neural Networks , 2015, MICCAI.

[21]  Jiming Li,et al.  Active learning for hyperspectral image classification with a stacked autoencoders based neural network , 2015, 2015 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS).

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

[23]  Sethuraman Panchanathan,et al.  Active Batch Selection via Convex Relaxations with Guaranteed Solution Bounds , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[24]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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

[26]  Jae Y. Shin,et al.  Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning? , 2016, IEEE transactions on medical imaging.

[27]  Bing Liu,et al.  Lifelong machine learning: a paradigm for continuous learning , 2017, Frontiers of Computer Science.

[28]  Joachim Denzler,et al.  Fine-Tuning Deep Neural Networks in Continuous Learning Scenarios , 2016, ACCV Workshops.

[29]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[30]  Ulrike von Luxburg,et al.  Feasibility of Active Machine Learning for Multiclass Compound Classification , 2016, J. Chem. Inf. Model..

[31]  Naif Alajlan,et al.  Deep learning approach for active classification of electrocardiogram signals , 2016, Inf. Sci..

[32]  Alexei A. Efros,et al.  Context Encoders: Feature Learning by Inpainting , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[33]  Alexei A. Efros,et al.  Colorful Image Colorization , 2016, ECCV.

[34]  Zoubin Ghahramani,et al.  Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning , 2015, ICML.

[35]  Murat Akçakaya,et al.  Classification Active Learning Based on Mutual Information , 2016, Entropy.

[36]  Razvan Pascanu,et al.  Overcoming catastrophic forgetting in neural networks , 2016, Proceedings of the National Academy of Sciences.

[37]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[38]  Lei Zhang,et al.  Fine-Tuning Convolutional Neural Networks for Biomedical Image Analysis: Actively and Incrementally , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[39]  Lin Yang,et al.  Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation , 2017, MICCAI.

[40]  Zoubin Ghahramani,et al.  Deep Bayesian Active Learning with Image Data , 2017, ICML.

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

[42]  Yefeng Zheng,et al.  Deep Learning and Convolutional Neural Networks for Medical Image Computing , 2017, Advances in Computer Vision and Pattern Recognition.

[43]  Jitendra Malik,et al.  Cost-Sensitive Active Learning for Intracranial Hemorrhage Detection , 2018, MICCAI.

[44]  Dorin Comaniciu,et al.  Learning to recognize Abnormalities in Chest X-Rays with Location-Aware Dense Networks , 2018, CIARP.

[45]  Daoqiang Zhang,et al.  Deep active learning for nucleus classification in pathology images , 2018, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).

[46]  Jean-Philippe Thiran,et al.  Efficient Active Learning for Image Classification and Segmentation using a Sample Selection and Conditional Generative Adversarial Network , 2018, MICCAI.

[47]  Simon K. Warfield,et al.  Active Deep Learning with Fisher Information for Patch-Wise Semantic Segmentation , 2018, DLMIA/ML-CDS@MICCAI.

[48]  Atsuto Maki,et al.  A systematic study of the class imbalance problem in convolutional neural networks , 2017, Neural Networks.

[49]  Zongwei Zhou,et al.  Integrating Active Learning and Transfer Learning for Carotid Intima-Media Thickness Video Interpretation , 2018, Journal of Digital Imaging.

[50]  Yifei Lu,et al.  Deep Active Self-paced Learning for Accurate Pulmonary Nodule Segmentation , 2018, MICCAI.

[51]  Orcun Goksel,et al.  Active Learning for Segmentation by Optimizing Content Information for Maximal Entropy , 2018, DLMIA/ML-CDS@MICCAI.

[52]  Yuxing Tang,et al.  Attention-Guided Curriculum Learning for Weakly Supervised Classification and Localization of Thoracic Diseases on Chest Radiographs , 2018, MLMI@MICCAI.

[53]  Raphaël Marée,et al.  Comparison of Deep Transfer Learning Strategies for Digital Pathology , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[54]  Evgenii Tsymbalov,et al.  Dropout-based Active Learning for Regression , 2018, AIST.

[55]  Andreas Nürnberger,et al.  The Power of Ensembles for Active Learning in Image Classification , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[56]  Bolei Zhou,et al.  Places: A 10 Million Image Database for Scene Recognition , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[57]  Frederik F. Flöther,et al.  Predicting the early risk of chronic kidney disease in patients with diabetes using real-world data , 2019, Nature Medicine.

[58]  Yiming Li,et al.  Semi-Supervised Brain Lesion Segmentation with an Adapted Mean Teacher Model , 2019, IPMI.

[59]  Chi-Wing Fu,et al.  Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation , 2019, MICCAI.

[60]  J. Jesu Vedha Nayahi,et al.  Medical Image Classification , 2019 .

[61]  Andre Esteva,et al.  A guide to deep learning in healthcare , 2019, Nature Medicine.

[62]  G. Corrado,et al.  End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography , 2019, Nature Medicine.

[63]  William Graf,et al.  Deep learning for cellular image analysis , 2019, Nature Methods.

[64]  Jun Akatsuka,et al.  Automated acquisition of explainable knowledge from unannotated histopathology images , 2019, Nature Communications.

[65]  Tina Olivia Sørlie Oftedal.  Uncertainty Measures and Transfer Learning in Active Learning for Text Classification , 2019 .

[66]  Kai Ma,et al.  Med3D: Transfer Learning for 3D Medical Image Analysis , 2019, ArXiv.

[67]  Stefan Wermter,et al.  Continual Lifelong Learning with Neural Networks: A Review , 2019, Neural Networks.

[68]  Nima Tajbakhsh,et al.  Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis , 2019, MICCAI.

[69]  Yifan Yu,et al.  CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison , 2019, AAAI.

[70]  Zongben Xu,et al.  An Active Deep Learning Approach for Minimally Supervised PolSAR Image Classification , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[71]  Yanbo Ma,et al.  Multi-attention Network for Thoracic Disease Classification and Localization , 2019, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[72]  Marleen de Bruijne,et al.  Semi-supervised Medical Image Segmentation via Learning Consistency Under Transformations , 2019, MICCAI.

[73]  Nima Tajbakhsh,et al.  Computer-aided detection and visualization of pulmonary embolism using a novel, compact, and discriminative image representation , 2019, Medical Image Anal..

[74]  LinLin Shen,et al.  Reverse active learning based atrous DenseNet for pathological image classification , 2019, BMC Bioinformatics.

[75]  Yiming Ding,et al.  A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using 18F-FDG PET of the Brain. , 2019, Radiology.

[76]  Simon K. Warfield,et al.  Intelligent Labeling Based on Fisher Information for Medical Image Segmentation Using Deep Learning , 2019, IEEE Transactions on Medical Imaging.

[77]  Hongzhi Yin,et al.  Streaming Session-based Recommendation , 2019, KDD.

[78]  Yue Huang,et al.  Cost-Effective Vehicle Type Recognition in Surveillance Images With Deep Active Learning and Web Data , 2020, IEEE Transactions on Intelligent Transportation Systems.

[79]  Hsuan-Tien Lin,et al.  Cold-start Active Learning through Self-Supervised Language Modeling , 2020, EMNLP.

[80]  Changjian Shui,et al.  Deep Active Learning: Unified and Principled Method for Query and Training , 2020, AISTATS.

[81]  Ruibin Feng,et al.  Parts2Whole: Self-supervised Contrastive Learning via Reconstruction , 2020, DART/DCL@MICCAI.

[82]  Xiaowei Ding,et al.  Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentation , 2019, Medical Image Anal..

[83]  J. Alison Noble,et al.  Uncertainty Estimates as Data Selection Criteria to Boost Omni-Supervised Learning , 2020, MICCAI.

[84]  Kai Ma,et al.  Rubik's Cube+: A self-supervised feature learning framework for 3D medical image analysis , 2020, Medical Image Anal..

[85]  Matthew P. Lungren,et al.  PENet—a scalable deep-learning model for automated diagnosis of pulmonary embolism using volumetric CT imaging , 2020, npj Digital Medicine.

[86]  Mohammad Reza Hosseinzadeh Taher,et al.  Learning Semantics-enriched Representation via Self-discovery, Self-classification, and Self-restoration , 2020, MICCAI.

[87]  Ross B. Girshick,et al.  Momentum Contrast for Unsupervised Visual Representation Learning , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[88]  Yaping Huang,et al.  Multi-label chest X-ray image classification via category-wise residual attention learning , 2020, Pattern Recognit. Lett..

[89]  Céline Hudelot,et al.  Active Learning for Imbalanced Datasets , 2020, 2020 IEEE Winter Conference on Applications of Computer Vision (WACV).

[90]  Jens Petersen,et al.  nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation , 2020, Nature Methods.

[91]  Nima Tajbakhsh,et al.  Extreme Consistency: Overcoming Annotation Scarcity and Domain Shifts , 2020, MICCAI.

[92]  Bernhard Kainz,et al.  A Survey on Active Learning and Human-in-the-Loop Deep Learning for Medical Image Analysis , 2019, Medical Image Anal..

[93]  Zongwei Zhou,et al.  Models Genesis , 2020, Medical Image Anal..

[94]  Hao Chen,et al.  Transformation-Consistent Self-Ensembling Model for Semisupervised Medical Image Segmentation , 2019, IEEE Transactions on Neural Networks and Learning Systems.