Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

[1]  Majid Nili Ahmadabadi,et al.  Multi-Representational Learning for Offline Signature Verification using Multi-Loss Snapshot Ensemble of CNNs , 2019, Expert Syst. Appl..

[2]  Jimeng Sun,et al.  RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism , 2016, NIPS.

[3]  Mohammed Ghazal,et al.  Alzheimer rsquo s disease diagnostics by a 3D deeply supervised adaptable convolutional network , 2018 .

[4]  Pascal Libuschewski,et al.  Group Equivariant Capsule Networks , 2018, NeurIPS.

[5]  MERIEM SEBAI,et al.  PartMitosis: A Partially Supervised Deep Learning Framework for Mitosis Detection in Breast Cancer Histopathology Images , 2020, IEEE Access.

[6]  Qi Bi,et al.  RADC-Net: A residual attention based convolution network for aerial scene classification , 2020, Neurocomputing.

[7]  Xiongwei Wu,et al.  Recent Advances in Deep Learning for Object Detection , 2019, Neurocomputing.

[8]  Wei Wang,et al.  Deep Learning for Single Image Super-Resolution: A Brief Review , 2018, IEEE Transactions on Multimedia.

[9]  Joan Bruna,et al.  Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation , 2014, NIPS.

[10]  Andrew Y. Ng,et al.  Parsing Natural Scenes and Natural Language with Recursive Neural Networks , 2011, ICML.

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

[12]  U. Rajendra Acharya,et al.  Convolutional neural networks for multi-class brain disease detection using MRI images , 2019, Comput. Medical Imaging Graph..

[13]  Ami Wiesel,et al.  ML for Flood Forecasting at Scale , 2019, ArXiv.

[14]  Matthieu Cord,et al.  Top-Down Regularization of Deep Belief Networks , 2013, NIPS.

[15]  Jiliang Tang,et al.  Characterizing the Decision Boundary of Deep Neural Networks , 2019, ArXiv.

[16]  Zijun Zhang,et al.  Improved Adam Optimizer for Deep Neural Networks , 2018, 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS).

[17]  Zhuowen Tu,et al.  Aggregated Residual Transformations for Deep Neural Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[18]  Cordelia Schmid,et al.  Incremental Learning of Object Detectors without Catastrophic Forgetting , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[19]  Nassir Navab,et al.  AggNet: Deep Learning From Crowds for Mitosis Detection in Breast Cancer Histology Images , 2016, IEEE Trans. Medical Imaging.

[20]  Yuan Zhang,et al.  Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network , 2019, IEEE Transactions on Smart Grid.

[21]  Aditya Khamparia,et al.  A Novel Transfer Learning Based Approach for Pneumonia Detection in Chest X-ray Images , 2020, Applied Sciences.

[22]  Abolfazl Attar,et al.  A modified deep convolutional neural network for detecting COVID-19 and pneumonia from chest X-ray images based on the concatenation of Xception and ResNet50V2 , 2020, Informatics in Medicine Unlocked.

[23]  Jon Kleinberg,et al.  Transfusion: Understanding Transfer Learning for Medical Imaging , 2019, NeurIPS.

[24]  Kibok Lee,et al.  Overcoming Catastrophic Forgetting With Unlabeled Data in the Wild , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[25]  Tan Nhat Pham,et al.  Early Disease Classification of Mango Leaves Using Feed-Forward Neural Network and Hybrid Metaheuristic Feature Selection , 2020, IEEE Access.

[26]  Muazzam Maqsood,et al.  A Deep Siamese Convolution Neural Network for Multi-Class Classification of Alzheimer Disease , 2020, Brain sciences.

[27]  Takashi Matsubara,et al.  Data Augmentation Using Random Image Cropping and Patching for Deep CNNs , 2018, IEEE Transactions on Circuits and Systems for Video Technology.

[28]  Kemal Polat,et al.  A Novel Medical Diagnosis model for COVID-19 infection detection based on Deep Features and Bayesian Optimization , 2020, Applied Soft Computing.

[29]  Refaat E Gabr,et al.  Brain and lesion segmentation in multiple sclerosis using fully convolutional neural networks: A large-scale study , 2020, Multiple sclerosis.

[30]  Prajoy Podder,et al.  Hybrid deep learning for detecting lung diseases from X-ray images , 2020, Informatics in Medicine Unlocked.

[31]  Lingjing Wang,et al.  Unsupervised 3D Reconstruction from a Single Image via Adversarial Learning , 2017, ArXiv.

[32]  Ankur Taly,et al.  Axiomatic Attribution for Deep Networks , 2017, ICML.

[33]  Poul Jennum,et al.  Design of a deep learning model for automatic scoring of periodic and non-periodic leg movements during sleep validated against multiple human experts. , 2020, Sleep medicine.

[34]  Léon Bottou,et al.  Large-Scale Machine Learning with Stochastic Gradient Descent , 2010, COMPSTAT.

[35]  Himanshu Asnani,et al.  LEARN Codes: Inventing Low-Latency Codes via Recurrent Neural Networks , 2020, IEEE Journal on Selected Areas in Information Theory.

[36]  Shadi Albarqouni,et al.  AggNet : Deep Learning From Crowds for Mitosis Detection in Breast Cancer Histology Images , 2016 .

[37]  R. Khonsari,et al.  Deep learning in medical image analysis: a third eye for doctors. , 2019, Journal of stomatology, oral and maxillofacial surgery.

[38]  Harris Drucker,et al.  Learning algorithms for classification: A comparison on handwritten digit recognition , 1995 .

[39]  Kai Hu,et al.  Brain Tumor Segmentation Using Multi-Cascaded Convolutional Neural Networks and Conditional Random Field , 2019, IEEE Access.

[40]  Taghi M. Khoshgoftaar,et al.  A survey on Image Data Augmentation for Deep Learning , 2019, Journal of Big Data.

[41]  Ivan Sipiran,et al.  3D Reconstruction of Incomplete Archaeological Objects Using a Generative Adversarial Network , 2017, CGI 2018.

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

[43]  Ellery Wulczyn,et al.  Deep learning-based survival prediction for multiple cancer types using histopathology images , 2019, PloS one.

[44]  Geoffrey E. Hinton.  Using fast weights to deblur old memories , 1987 .

[45]  Erik Cambria,et al.  Recent Trends in Deep Learning Based Natural Language Processing , 2017, IEEE Comput. Intell. Mag..

[46]  Yu Li,et al.  Accelerating Flash Calculation through Deep Learning Methods , 2018, J. Comput. Phys..

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

[48]  Ying Zhang,et al.  Batch normalized recurrent neural networks , 2015, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[49]  Shane Legg,et al.  Human-level control through deep reinforcement learning , 2015, Nature.

[50]  Alok Porwal,et al.  Capsulenet-Based Spatial–Spectral Classifier for Hyperspectral Images , 2019, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[51]  Michael T. Lu,et al.  Deep convolutional neural networks to predict cardiovascular risk from computed tomography , 2021, Nature Communications.

[52]  Sebastian Risi,et al.  Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks , 2017, Neural Networks.

[53]  Zheng Liu,et al.  Optogenetics inspired transition metal dichalcogenide neuristors for in-memory deep recurrent neural networks , 2020, Nature Communications.

[54]  Geoffrey E. Hinton,et al.  Distilling the Knowledge in a Neural Network , 2015, ArXiv.

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

[56]  Taghi M. Khoshgoftaar,et al.  Survey on deep learning with class imbalance , 2019, J. Big Data.

[57]  Yang Yi,et al.  Compact video fingerprinting via an improved capsule net , 2020 .

[58]  Marc Peter Deisenroth,et al.  Deep Reinforcement Learning: A Brief Survey , 2017, IEEE Signal Processing Magazine.

[59]  Christopher Potts,et al.  Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank , 2013, EMNLP.

[60]  Andrew Y. Ng,et al.  CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning , 2017, ArXiv.

[61]  M. Shamim Hossain,et al.  MetaCOVID: A Siamese neural network framework with contrastive loss for n-shot diagnosis of COVID-19 patients , 2020, Pattern Recognition.

[62]  Hieu Trung Huynh,et al.  Joint Age Estimation and Gender Classification of Asian Faces Using Wide ResNet , 2020, SN Comput. Sci..

[63]  Xiao Xiang Zhu,et al.  Learning to Pay Attention on Spectral Domain: A Spectral Attention Module-Based Convolutional Network for Hyperspectral Image Classification , 2020, IEEE Transactions on Geoscience and Remote Sensing.

[64]  Samy Bengio,et al.  Understanding deep learning (still) requires rethinking generalization , 2021, Commun. ACM.

[65]  Yu Li,et al.  On the Decision Boundary of Deep Neural Networks , 2018, ArXiv.

[66]  Jack Tulloch,et al.  Machine Learning in the Prevention, Diagnosis and Management of Diabetic Foot Ulcers: A Systematic Review , 2020, IEEE Access.

[67]  Stefan Wermter,et al.  Lifelong learning of human actions with deep neural network self-organization , 2017, Neural Networks.

[68]  Abeer Alsadoon,et al.  A novel solution of using deep learning for left ventricle detection: Enhanced feature extraction , 2020, Comput. Methods Programs Biomed..

[69]  Yan Liu,et al.  Deep residual learning for image steganalysis , 2018, Multimedia Tools and Applications.

[70]  Ronald M. Summers,et al.  Anatomy-specific classification of medical images using deep convolutional nets , 2015, 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI).

[71]  Daniel S. Kermany,et al.  Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning , 2018, Cell.

[72]  M. Viergever,et al.  Automatic Segmentation of MR Brain Images With a Convolutional Neural Network. , 2016, IEEE transactions on medical imaging.

[73]  Nasir M. Rajpoot,et al.  Locality Sensitive Deep Learning for Detection and Classification of Nuclei in Routine Colon Cancer Histology Images , 2016, IEEE Trans. Medical Imaging.

[74]  Laurence T. Yang,et al.  Convolutional neural networks for medical image analysis: State-of-the-art, comparisons, improvement and perspectives , 2021, Neurocomputing.

[75]  Mohd Asyraf Zulkifley,et al.  Pterygium-Net: a deep learning approach to pterygium detection and localization , 2019, Multimedia Tools and Applications.

[76]  Hong Yu,et al.  Structured prediction models for RNN based sequence labeling in clinical text , 2016, EMNLP.

[77]  Taghi M. Khoshgoftaar,et al.  A survey of transfer learning , 2016, Journal of Big Data.

[78]  HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[79]  O. Troyanskaya,et al.  Predicting effects of noncoding variants with deep learning–based sequence model , 2015, Nature Methods.

[80]  Hyojin Kim,et al.  LBANN: livermore big artificial neural network HPC toolkit , 2015, MLHPC@SC.

[81]  Guodong Zhang,et al.  Three Mechanisms of Weight Decay Regularization , 2018, ICLR.

[82]  Nenghai Yu,et al.  Semantics-Preserving Bag-of-Words Models and Applications , 2010, IEEE Transactions on Image Processing.

[83]  Christoph Goller,et al.  Learning task-dependent distributed representations by backpropagation through structure , 1996, Proceedings of International Conference on Neural Networks (ICNN'96).

[84]  Yabo Fu,et al.  Deep Learning in Medical Image Registration: A Review , 2020, Physics in medicine and biology.

[85]  Haijun Lei,et al.  Attention-Guided Multi-Branch Convolutional Neural Network for Mitosis Detection From Histopathological Images , 2020, IEEE Journal of Biomedical and Health Informatics.

[86]  Pascal Vincent,et al.  Visualizing Higher-Layer Features of a Deep Network , 2009 .

[87]  D. Hubel,et al.  Receptive fields, binocular interaction and functional architecture in the cat's visual cortex , 1962, The Journal of physiology.

[88]  Jun Zhang,et al.  Learning to Read Chest X-Ray Images from 16000+ Examples Using CNN , 2017, 2017 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE).

[89]  Deok-Jin Lee,et al.  Deep Learning-Based Real-Time Multiple-Object Detection and Tracking from Aerial Imagery via a Flying Robot with GPU-Based Embedded Devices , 2019, Sensors.

[90]  Zhang Zhang,et al.  An interpretable deep-learning architecture of capsule networks for identifying cell-type gene expression programs from single-cell RNA-sequencing data , 2020, Nature Machine Intelligence.

[91]  Dinggang Shen,et al.  Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation, and Diagnosis for COVID-19 , 2020, IEEE Reviews in Biomedical Engineering.

[92]  Ting Guo,et al.  Teeth category classification via seven‐layer deep convolutional neural network with max pooling and global average pooling , 2019, Int. J. Imaging Syst. Technol..

[93]  Samy Bengio,et al.  Understanding deep learning requires rethinking generalization , 2016, ICLR.

[94]  Anne L. Martel,et al.  Deep neural network models for computational histopathology: A survey , 2019, Medical Image Anal..

[95]  Mei-Ling Shyu,et al.  A Survey on Deep Learning , 2018, ACM Comput. Surv..

[96]  Thomas B. Schön,et al.  Beyond exploding and vanishing gradients: analysing RNN training using attractors and smoothness , 2019, AISTATS.

[97]  Mohammed A. Fadhel,et al.  Optimizing the Performance of Breast Cancer Classification by Employing the Same Domain Transfer Learning from Hybrid Deep Convolutional Neural Network Model , 2020, Electronics.

[98]  Zili Zhang,et al.  Sample Subset Optimization Techniques for Imbalanced and Ensemble Learning Problems in Bioinformatics Applications , 2014, IEEE Transactions on Cybernetics.

[99]  Antonella Santone,et al.  Explainable Deep Learning for Pulmonary Disease and Coronavirus COVID-19 Detection from X-rays , 2020, Computer Methods and Programs in Biomedicine.

[100]  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.

[101]  Nassir Navab,et al.  Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks , 2018, MICCAI.

[102]  Ioannis D. Apostolopoulos,et al.  Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks , 2020, Physical and Engineering Sciences in Medicine.

[103]  Tao Wang,et al.  A hybrid method for heartbeat classification via convolutional neural networks, multilayer perceptrons and focal loss , 2020, PeerJ Comput. Sci..

[104]  Daniel Roggen,et al.  Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition , 2016, Sensors.

[105]  Ishwar K. Sethi,et al.  Confidence-based classifier design , 2006, Pattern Recognit..

[106]  Jasmine Banks,et al.  Benchmarking HEp-2 specimen cells classification using linear discriminant analysis on higher order spectra features of cell shape , 2019, Pattern Recognit. Lett..

[107]  Madhu S. Nair,et al.  Computer-aided detection of COVID-19 from X-ray images using multi-CNN and Bayesnet classifier , 2020, Biocybernetics and Biomedical Engineering.

[108]  Filiz Bunyak,et al.  Deep learning-based face analysis system for monitoring customer interest , 2019, Journal of Ambient Intelligence and Humanized Computing.

[109]  Iasonas Kokkinos,et al.  DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[110]  Christopher Joseph Pal,et al.  Brain tumor segmentation with Deep Neural Networks , 2015, Medical Image Anal..

[111]  Mika S. Jain,et al.  Predicting tumour mutational burden from histopathological images using multiscale deep learning , 2020 .

[112]  C. Bergmeir,et al.  Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions , 2019, International Journal of Forecasting.

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

[114]  Nassir Navab,et al.  A Taxonomy and Library for Visualizing Learned Features in Convolutional Neural Networks , 2016, ArXiv.

[115]  Chris Yakopcic,et al.  A State-of-the-Art Survey on Deep Learning Theory and Architectures , 2019, Electronics.

[116]  Karl Thurnhofer-Hemsi,et al.  A Convolutional Neural Network Framework for Accurate Skin Cancer Detection , 2020, Neural Processing Letters.

[117]  N. Rajpoot,et al.  Locality Sensitive Deep Learning for Detection and Classification of Nuclei in Routine Colon Cancer Histology Images , 2016, IEEE Transactions on Medical Imaging.

[118]  Daniel Durstewitz,et al.  Deep learning for small and big data in psychiatry , 2020, Neuropsychopharmacology.

[119]  Xu Yang,et al.  An Xception Convolutional Neural Network for Malware Classification with Transfer Learning , 2019, 2019 10th IFIP International Conference on New Technologies, Mobility and Security (NTMS).

[120]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[121]  Chong Wang,et al.  Real-Time Underwater Image Recognition with FPGA Embedded System for Convolutional Neural Network , 2019, Sensors.

[122]  Bingsheng He,et al.  Efficient Memory Management for GPU-based Deep Learning Systems , 2019, ArXiv.

[123]  Jiaqi Gu,et al.  DREAMPlace: Deep Learning Toolkit-Enabled GPU Acceleration for Modern VLSI Placement , 2020, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

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

[125]  Yulia Dodonova,et al.  Residual and plain convolutional neural networks for 3D brain MRI classification , 2017, 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017).

[126]  Omran Al-Shamma,et al.  DFU_QUTNet: diabetic foot ulcer classification using novel deep convolutional neural network , 2019, Multimedia Tools and Applications.

[127]  Serestina Viriri,et al.  Deep learning techniques for skin lesion analysis and melanoma cancer detection: a survey of state-of-the-art , 2020, Artificial Intelligence Review.

[128]  Sheng Wang,et al.  AUC-maximized Deep Convolutional Neural Fields for Sequence Labeling , 2015, ArXiv.

[129]  Xindong Wu,et al.  Object Detection With Deep Learning: A Review , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[130]  Tianqi Chen,et al.  Empirical Evaluation of Rectified Activations in Convolutional Network , 2015, ArXiv.

[131]  Nishita Mehta,et al.  Concurrence of big data analytics and healthcare: A systematic review , 2018, Int. J. Medical Informatics.

[132]  Shaoyi Du,et al.  A review of object detection based on deep learning , 2020, Multimedia Tools and Applications.

[133]  Juntao Guan,et al.  Fixed Pattern Noise Reduction for Infrared Images Based on Cascade Residual Attention CNN , 2019, Neurocomputing.

[134]  Sparsh Mittal,et al.  A survey of techniques for optimizing deep learning on GPUs , 2019, J. Syst. Archit..

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

[136]  Talha Burak Alakus,et al.  Convolutional capsnet: A novel artificial neural network approach to detect COVID-19 disease from X-ray images using capsule networks , 2020, Chaos, Solitons & Fractals.

[137]  Shivajirao M. Jadhav,et al.  Deep convolutional neural network based medical image classification for disease diagnosis , 2019, Journal of Big Data.

[138]  Anuj Bhardwaj,et al.  A review on brain tumor segmentation of MRI images. , 2019, Magnetic resonance imaging.

[139]  Robin Aly,et al.  Identifying child abuse through text mining and machine learning , 2017, Expert Syst. Appl..

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

[141]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[142]  Amir Hussain,et al.  Contextual Audio-Visual Switching For Speech Enhancement in Real-World Environments , 2018, Inf. Fusion.

[143]  Neil D. Reeves,et al.  DFUNet: Convolutional Neural Networks for Diabetic Foot Ulcer Classification , 2017, IEEE Transactions on Emerging Topics in Computational Intelligence.

[144]  Geoffrey E. Hinton,et al.  Regularizing Neural Networks by Penalizing Confident Output Distributions , 2017, ICLR.

[145]  Ronald M. Summers,et al.  ChestX-ray: Hospital-Scale Chest X-ray Database and Benchmarks on Weakly Supervised Classification and Localization of Common Thorax Diseases , 2019, Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics.

[146]  Jianzhong Wu,et al.  Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images , 2016, IEEE Transactions on Medical Imaging.

[147]  Anne E Carpenter,et al.  Opportunities and obstacles for deep learning in biology and medicine , 2017, bioRxiv.

[148]  Song Han,et al.  Deep Compression: Compressing Deep Neural Network with Pruning, Trained Quantization and Huffman Coding , 2015, ICLR.

[149]  Tara N. Sainath,et al.  Improving deep neural networks for LVCSR using rectified linear units and dropout , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[150]  Jun Wang,et al.  Boosted EfficientNet: Detection of Lymph Node Metastases in Breast Cancer Using Convolutional Neural Networks , 2020, Cancers.

[151]  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).

[152]  Loris Nanni,et al.  Fair comparison of skin detection approaches on publicly available datasets , 2018, Expert Syst. Appl..

[153]  David Gschwend,et al.  ZynqNet: An FPGA-Accelerated Embedded Convolutional Neural Network , 2020, ArXiv.

[154]  Yu Li,et al.  Deep learning in bioinformatics: introduction, application, and perspective in big data era , 2019, bioRxiv.

[155]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[156]  Dinggang Shen,et al.  Deep Learning Based Imaging Data Completion for Improved Brain Disease Diagnosis , 2014, MICCAI.

[157]  Victor Alves,et al.  Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images , 2016, IEEE Transactions on Medical Imaging.

[158]  Laith Alzubaidi,et al.  Review of the State of the Art of Deep Learning for Plant Diseases: A Broad Analysis and Discussion , 2020, Plants.

[159]  Hsiao-Chi Li,et al.  Lightweight and Resource-Constrained Learning Network for Face Recognition with Performance Optimization , 2020, Sensors.

[160]  B. Vincenzi,et al.  Deep Learning Algorithm Trained with COVID-19 Pneumonia Also Identifies Immune Checkpoint Inhibitor Therapy-Related Pneumonitis , 2021, Cancers.

[161]  François Chollet,et al.  Xception: Deep Learning with Depthwise Separable Convolutions , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[162]  Wenwu Zhu,et al.  Deep Learning on Graphs: A Survey , 2018, IEEE Transactions on Knowledge and Data Engineering.

[163]  Hongbin Zha,et al.  Non-Rigid 2D-3D Registration Using Convolutional Autoencoders , 2020, 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI).

[164]  Lucien E. Weiss,et al.  DeepSTORM3D: dense 3D localization microscopy and PSF design by deep learning , 2020, Nature Methods.

[165]  Francis C. M. Lau,et al.  HiveD: Sharing a GPU Cluster for Deep Learning with Guarantees , 2020, OSDI.

[166]  Ninon Burgos,et al.  Convolutional Neural Networks for Classification of Alzheimer's Disease: Overview and Reproducible Evaluation , 2019, Medical Image Anal..

[167]  Yurong Liu,et al.  A survey of deep neural network architectures and their applications , 2017, Neurocomputing.

[168]  Zuo Min.  PUBLIC WELFARE ORGANIZATION MANAGEMENT SYSTEM BASED ON FPGA AND DEEP LEARNING , 2020 .

[169]  Catherine D. Schuman,et al.  A Study of Complex Deep Learning Networks on High Performance, Neuromorphic, and Quantum Computers , 2016, 2016 2nd Workshop on Machine Learning in HPC Environments (MLHPC).

[170]  S. Nabavi,et al.  Hebbian plasticity in vivo: relevance and induction , 2017, Current Opinion in Neurobiology.

[171]  Daniel L. Rubin,et al.  Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions , 2017, Journal of Digital Imaging.

[172]  Ellery Wulczyn,et al.  Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer , 2018, npj Digital Medicine.

[173]  Feng Lv,et al.  Efficient densely connected convolutional neural networks , 2021, Pattern Recognit..

[174]  Simon Hunter,et al.  Randomized controlled trial of a 12-week digital care program in improving low back pain , 2019, npj Digital Medicine.

[175]  Yang Zhao,et al.  Deep High-Resolution Representation Learning for Visual Recognition , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[176]  E.O. David,et al.  Skin cancer detection by deep learning and sound analysis algorithms: A prospective clinical study of an elementary dermoscope , 2019, EBioMedicine.

[177]  Min Beom Lee,et al.  Artificial Intelligence-Based Mitosis Detection in Breast Cancer Histopathology Images Using Faster R-CNN and Deep CNNs , 2020, Journal of clinical medicine.

[178]  Chih-Hsien Hsia,et al.  An Automated Biometric Identification System Using CNN-Based Palm Vein Recognition , 2020, 2020 International Conference on Advanced Robotics and Intelligent Systems (ARIS).

[179]  Manuel J. Marín-Jiménez,et al.  Energy‐based tuning of convolutional neural networks on multi‐GPUs , 2019, Concurr. Comput. Pract. Exp..

[180]  Mita Nasipuri,et al.  Patch-based system for Classification of Breast Histology images using deep learning , 2019, Comput. Medical Imaging Graph..

[181]  Diane J. Cook,et al.  Transfer learning for activity recognition: a survey , 2013, Knowledge and Information Systems.

[182]  Ausif Mahmood,et al.  Review of Deep Learning Algorithms and Architectures , 2019, IEEE Access.

[183]  Sebastian Ruder,et al.  An overview of gradient descent optimization algorithms , 2016, Vestnik komp'iuternykh i informatsionnykh tekhnologii.

[184]  Ching-Te Chiu,et al.  Filter-based Deep-Compression with Global Average Pooling for Convolutional Networks , 2018, 2018 IEEE International Workshop on Signal Processing Systems (SiPS).

[185]  Daniel Urda,et al.  Forward Noise Adjustment Scheme for Data Augmentation , 2018, 2018 IEEE Symposium Series on Computational Intelligence (SSCI).

[186]  Franca Garzotto,et al.  Content-Based Video Recommendation System Based on Stylistic Visual Features , 2016, Journal on Data Semantics.

[187]  Zhiguang Cao,et al.  Distilling the Knowledge From Handcrafted Features for Human Activity Recognition , 2018, IEEE Transactions on Industrial Informatics.

[188]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[189]  Z. Jane Wang,et al.  A CNN Regression Approach for Real-Time 2D/3D Registration , 2016, IEEE Transactions on Medical Imaging.

[190]  Ting Liu,et al.  Recent advances in convolutional neural networks , 2015, Pattern Recognit..

[191]  W. Gerstner,et al.  The temporal paradox of Hebbian learning and homeostatic plasticity , 2017, Current Opinion in Neurobiology.

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

[193]  M. N. Sulaiman,et al.  A Review On Evaluation Metrics For Data Classification Evaluations , 2015 .

[194]  Carlos Alberto Silva,et al.  Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images. , 2016, IEEE transactions on medical imaging.

[195]  Ching-Te Chiu,et al.  Filter-based deep-compression with global average pooling for convolutional networks , 2019, J. Syst. Archit..

[196]  M. Mohammed Thaha,et al.  Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images , 2019, Journal of Medical Systems.

[197]  Jianlin Cheng,et al.  DeepCryoPicker: fully automated deep neural network for single protein particle picking in cryo-EM , 2019, BMC Bioinformatics.

[198]  Eklas Hossain,et al.  Application of Big Data and Machine Learning in Smart Grid, and Associated Security Concerns: A Review , 2019, IEEE Access.

[199]  Ping Kuang,et al.  Image super-resolution with densely connected convolutional networks , 2018, Applied Intelligence.

[200]  Charles X. Ling,et al.  Using AUC and accuracy in evaluating learning algorithms , 2005, IEEE Transactions on Knowledge and Data Engineering.

[201]  Ashok Veeraraghavan,et al.  Vision and Deep Learning-Based Algorithms to Detect and Quantify Cracks on Concrete Surfaces from UAV Videos , 2020, Sensors.

[202]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[203]  Khalid M. Hosny,et al.  Skin Cancer Classification using Deep Learning and Transfer Learning , 2018, 2018 9th Cairo International Biomedical Engineering Conference (CIBEC).

[204]  Nassir Navab,et al.  Recalibrating Fully Convolutional Networks With Spatial and Channel “Squeeze and Excitation” Blocks , 2018, IEEE Transactions on Medical Imaging.

[205]  Sepp Hochreiter,et al.  The Vanishing Gradient Problem During Learning Recurrent Neural Nets and Problem Solutions , 1998, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[206]  Geraint Rees,et al.  Clinically applicable deep learning for diagnosis and referral in retinal disease , 2018, Nature Medicine.

[207]  Jianlin Cheng,et al.  DeepCryoPicker: fully automated deep neural network for single protein particle picking in cryo-EM , 2020, BMC bioinformatics.

[208]  Razvan Pascanu,et al.  How to Construct Deep Recurrent Neural Networks , 2013, ICLR.

[209]  Hesham F. A. Hamed,et al.  Two-phase multi-model automatic brain tumour diagnosis system from magnetic resonance images using convolutional neural networks , 2018, EURASIP Journal on Image and Video Processing.

[210]  Xin Yan,et al.  Collaborative Edge Computing With FPGA-Based CNN Accelerators for Energy-Efficient and Time-Aware Face Tracking System , 2021, IEEE Transactions on Computational Social Systems.

[211]  Doina Precup,et al.  Exploring uncertainty measures in deep networks for Multiple sclerosis lesion detection and segmentation , 2020, Medical Image Anal..

[212]  Zhigang Zeng,et al.  CLU-CNNs: Object detection for medical images , 2019, Neurocomputing.

[213]  Kilian Q. Weinberger,et al.  Deep Networks with Stochastic Depth , 2016, ECCV.

[214]  Malrey Lee,et al.  The skin cancer classification using deep convolutional neural network , 2018, Multimedia Tools and Applications.

[215]  Chao Yang,et al.  A Survey on Deep Transfer Learning , 2018, ICANN.

[216]  Justin Salamon,et al.  Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification , 2016, IEEE Signal Processing Letters.

[217]  Enhua Wu,et al.  Squeeze-and-Excitation Networks , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[218]  Özlem Batur Dinler,et al.  An Optimal Feature Parameter Set Based on Gated Recurrent Unit Recurrent Neural Networks for Speech Segment Detection , 2020, Applied Sciences.

[219]  Zhiyong Xu,et al.  A Noise-Robust Framework for Automatic Segmentation of COVID-19 Pneumonia Lesions From CT Images , 2020, IEEE Transactions on Medical Imaging.

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

[221]  Pingkun Yan,et al.  Deep learning in medical image registration: a survey , 2020, Machine Vision and Applications.

[222]  Max A. Viergever,et al.  Automatic Segmentation of MR Brain Images With a Convolutional Neural Network , 2016, IEEE Transactions on Medical Imaging.

[223]  David J. Hand,et al.  A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems , 2001, Machine Learning.

[224]  Pauley Chea,et al.  Current applications and future directions of deep learning in musculoskeletal radiology , 2019, Skeletal Radiology.

[225]  Doina Precup,et al.  Exploring Uncertainty Measures in Deep Networks for Multiple Sclerosis Lesion Detection and Segmentation , 2018, MICCAI.

[226]  Hugo Larochelle,et al.  Efficient Learning of Deep Boltzmann Machines , 2010, AISTATS.

[227]  Gregor Urban,et al.  Inner and Outer Recursive Neural Networks for Chemoinformatics Applications , 2018, J. Chem. Inf. Model..

[228]  Hilde van der Togt,et al.  Publisher's Note , 2003, J. Netw. Comput. Appl..

[229]  Alexander D'Amour,et al.  Underspecification Presents Challenges for Credibility in Modern Machine Learning , 2020, J. Mach. Learn. Res..

[230]  Beate Sick,et al.  Integrating uncertainty in deep neural networks for MRI based stroke analysis , 2020, Medical Image Anal..

[231]  Luca Maria Gambardella,et al.  Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks , 2013, MICCAI.

[232]  Peng Liu,et al.  A review of lane detection methods based on deep learning , 2021, Pattern Recognit..

[233]  He Chen,et al.  FPGA Implementation for CNN-Based Optical Remote Sensing Object Detection , 2021, Electronics.

[234]  Tao Zhang,et al.  Model Compression and Acceleration for Deep Neural Networks: The Principles, Progress, and Challenges , 2018, IEEE Signal Processing Magazine.

[235]  Javier Escudero,et al.  An Efficient Mixture of Deep and Machine Learning Models for COVID-19 and Tuberculosis Detection Using X-Ray Images in Resource Limited Settings , 2020, Artificial Intelligence for COVID-19.

[236]  Kaijin Xu,et al.  A Deep Learning System to Screen Novel Coronavirus Disease 2019 Pneumonia , 2020, Engineering.

[237]  Joel J. P. C. Rodrigues,et al.  A novel deep learning based framework for the detection and classification of breast cancer using transfer learning , 2019, Pattern Recognit. Lett..

[238]  Abeer Mohsin Saleh,et al.  Analysis and best parameters selection for person recognition based on gait model using CNN algorithm and image augmentation , 2021, J. Big Data.

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

[240]  Ayman El-Baz,et al.  Alzheimer's disease diagnostics by a 3D deeply supervised adaptable convolutional network. , 2018, Frontiers in bioscience.

[241]  Andrew Godbout,et al.  Volume-preserving Neural Networks: A Solution to the Vanishing Gradient Problem , 2019, ArXiv.

[242]  Jürgen Schmidhuber,et al.  Highway Networks , 2015, ArXiv.

[243]  Friedhelm Schwenker,et al.  Deep Learning in Selected Cancers’ Image Analysis—A Survey , 2020, J. Imaging.

[244]  Taghi M. Khoshgoftaar,et al.  Deep learning applications and challenges in big data analytics , 2015, Journal of Big Data.

[245]  U. Rajendra Acharya,et al.  Automated invasive ductal carcinoma detection based using deep transfer learning with whole-slide images , 2020, Pattern Recognit. Lett..

[246]  Morteza Heidari,et al.  Improving the performance of CNN to predict the likelihood of COVID-19 using chest X-ray images with preprocessing algorithms , 2020, International Journal of Medical Informatics.

[247]  Shaikh Anowarul Fattah,et al.  CovXNet: A multi-dilation convolutional neural network for automatic COVID-19 and other pneumonia detection from chest X-ray images with transferable multi-receptive feature optimization , 2020, Computers in Biology and Medicine.

[248]  Simon J. Doran,et al.  Stacked Autoencoders for Unsupervised Feature Learning and Multiple Organ Detection in a Pilot Study Using 4D Patient Data , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[249]  Xinggang Wang,et al.  MaskMitosis: a deep learning framework for fully supervised, weakly supervised, and unsupervised mitosis detection in histopathology images , 2020, Medical & Biological Engineering & Computing.

[250]  Yi Wang,et al.  ReLTanh: An activation function with vanishing gradient resistance for SAE-based DNNs and its application to rotating machinery fault diagnosis , 2019, Neurocomputing.

[251]  Mohammed A. Fadhel,et al.  Towards a Better Understanding of Transfer Learning for Medical Imaging: A Case Study , 2020, Applied Sciences.

[252]  Ingo Steinwart,et al.  Consistency of support vector machines and other regularized kernel classifiers , 2005, IEEE Transactions on Information Theory.

[253]  Pierrick Coupé,et al.  AssemblyNet: A large ensemble of CNNs for 3D whole brain MRI segmentation , 2019, NeuroImage.

[254]  Lizhe Wang,et al.  Adaptive Spatial-Scale-Aware Deep Convolutional Neural Network for High-Resolution Remote Sensing Imagery Scene Classification , 2018, IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium.

[255]  Lin Wang,et al.  Multi-Resolution CNN and Knowledge Transfer for Candidate Classification in Lung Nodule Detection , 2019, IEEE Access.

[256]  Debotosh Bhattacharjee,et al.  EnsemConvNet: a deep learning approach for human activity recognition using smartphone sensors for healthcare applications , 2020, Multimedia Tools and Applications.

[257]  Lei Xing,et al.  Markerless pancreatic tumor target localization enabled by deep learning. , 2019, International journal of radiation oncology, biology, physics.

[258]  Sergey Ioffe,et al.  Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning , 2016, AAAI.

[259]  Abhinav Vishnu,et al.  Deep learning for computational chemistry , 2017, J. Comput. Chem..

[260]  Arvid Lundervold,et al.  An overview of deep learning in medical imaging focusing on MRI , 2018, Zeitschrift fur medizinische Physik.

[261]  Mei-Ling Shyu,et al.  Evolutionary Programming Based Deep Learning Feature Selection and Network Construction for Visual Data Classification , 2020, Inf. Syst. Frontiers.

[262]  Michael Blum,et al.  High-Throughput Classification of Radiographs Using Deep Convolutional Neural Networks , 2016, Journal of Digital Imaging.

[263]  Boris Hanin,et al.  Which Neural Net Architectures Give Rise To Exploding and Vanishing Gradients? , 2018, NeurIPS.

[264]  Saddam Hussain Khan,et al.  Classification and Region Analysis of COVID-19 Infection using Lung CT Images and Deep Convolutional Neural Networks , 2020, SSRN Electronic Journal.

[265]  Jürgen Schmidhuber,et al.  Multi-column deep neural network for traffic sign classification , 2012, Neural Networks.

[266]  Lipo Wang,et al.  Deep Learning Applications in Medical Image Analysis , 2018, IEEE Access.

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

[268]  Francisco Herrera,et al.  BreakHis based breast cancer automatic diagnosis using deep learning: Taxonomy, survey and insights , 2020, Neurocomputing.

[269]  Bo Zhou,et al.  Convolutional neural networks for computer-aided detection or diagnosis in medical image analysis: An overview. , 2019, Mathematical biosciences and engineering : MBE.

[270]  Mohammad Behdad Mohammad Behdad Jamshidi Jamshidi,et al.  Artificial Intelligence and COVID-19: Deep Learning Approaches for Diagnosis and Treatment , 2020, Ieee Access.

[271]  Omran Al-Shamma,et al.  Boosting Convolutional Neural Networks Performance Based on FPGA Accelerator , 2018, ISDA.

[272]  Mohammed A. Fadhel,et al.  Deep Learning Models for Classification of Red Blood Cells in Microscopy Images to Aid in Sickle Cell Anemia Diagnosis , 2020, Electronics.

[273]  Max A. Viergever,et al.  A deep learning framework for unsupervised affine and deformable image registration , 2018, Medical Image Anal..

[274]  Yong Xia,et al.  Autonomous Deep Learning: A Genetic DCNN Designer for Image Classification , 2018, Neurocomputing.

[275]  Xianfeng Zhao,et al.  JPEG steganalysis based on ResNeXt with Gauss partial derivative filters , 2020, Multimedia Tools and Applications.

[276]  Ni Zhang,et al.  Skin cancer diagnosis based on optimized convolutional neural network , 2020, Artif. Intell. Medicine.

[277]  Geoffrey E. Hinton,et al.  Dynamic Routing Between Capsules , 2017, NIPS.

[278]  F. Roca,et al.  An Overview on the Latest Nature-Inspired and Metaheuristics-Based Image Registration Algorithms , 2020, Applied Sciences.

[279]  Christoph H. Lampert,et al.  iCaRL: Incremental Classifier and Representation Learning , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[280]  B. Furht,et al.  Deep Learning applications for COVID-19 , 2021, Journal of Big Data.

[281]  Qiang Chen,et al.  Network In Network , 2013, ICLR.

[282]  A. S. Jalal,et al.  Deep learning-based ResNeXt model in phycological studies for future , 2020 .

[283]  Seyed-Ahmad Ahmadi,et al.  V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation , 2016, 2016 Fourth International Conference on 3D Vision (3DV).

[284]  Nikos Komodakis,et al.  Wide Residual Networks , 2016, BMVC.

[285]  Ming Zhang,et al.  Overfitting remedy by sparsifying regularization on fully-connected layers of CNNs , 2019, Neurocomputing.

[286]  Ahmad Shawahna,et al.  FPGA-Based Accelerators of Deep Learning Networks for Learning and Classification: A Review , 2019, IEEE Access.

[287]  Sergey Ioffe,et al.  Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[288]  Pramod K. Varshney,et al.  Long short-term memory-based deep recurrent neural networks for target tracking , 2019, Inf. Sci..

[289]  Wei Zhao,et al.  COVID-19 Chest CT Image Segmentation - A Deep Convolutional Neural Network Solution , 2020, ArXiv.

[290]  Ding-Xuan Zhou,et al.  Theory of deep convolutional neural networks: Downsampling , 2020, Neural Networks.

[291]  Asifullah Khan,et al.  A survey of the recent architectures of deep convolutional neural networks , 2019, Artificial Intelligence Review.

[292]  Chao Di,et al.  U1 snRNP regulates cancer cell migration and invasion in vitro , 2020, Nature Communications.

[293]  Jonathan Rubin,et al.  Densely connected convolutional networks for detection of atrial fibrillation from short single-lead ECG recordings. , 2018, Journal of electrocardiology.

[294]  Zdeněk Peroutka,et al.  Artificial Intelligence and COVID-19: Deep Learning Approaches for Diagnosis and Treatment , 2020, IEEE Access.

[295]  Mohammed A. Fadhel,et al.  Multi-class Breast Cancer Classification by a Novel Two-Branch Deep Convolutional Neural Network Architecture , 2019, 2019 12th International Conference on Developments in eSystems Engineering (DeSE).

[296]  Pedro M. Domingos,et al.  Tree Induction for Probability-Based Ranking , 2003, Machine Learning.

[297]  Takuya Ueda,et al.  Effects of data count and image scaling on Deep Learning training , 2020, PeerJ Comput. Sci..

[298]  Javier Del Ser,et al.  Deep Learning for Multigrade Brain Tumor Classification in Smart Healthcare Systems: A Prospective Survey , 2020, IEEE Transactions on Neural Networks and Learning Systems.

[299]  Mohammad Teshnehlab,et al.  A Robust Sentiment Analysis Method Based on Sequential Combination of Convolutional and Recursive Neural Networks , 2019, Neural Processing Letters.

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

[301]  Khalid M. Hosny,et al.  Skin Lesions Classification Into Eight Classes for ISIC 2019 Using Deep Convolutional Neural Network and Transfer Learning , 2020, IEEE Access.

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

[303]  Hongyu Wang,et al.  End-to-End Image Super-Resolution via Deep and Shallow Convolutional Networks , 2016, IEEE Access.

[304]  Changxing Ding,et al.  Dual-force convolutional neural networks for accurate brain tumor segmentation , 2019, Pattern Recognit..

[305]  Sreeram Kannan,et al.  LEARN Codes: Inventing Low-Latency Codes via Recurrent Neural Networks , 2018, ICC 2019 - 2019 IEEE International Conference on Communications (ICC).

[306]  Niloy J. Mitra,et al.  Learning Semantic Deformation Flows with 3D Convolutional Networks , 2016, ECCV.

[307]  Wei Shen,et al.  Multi-scale Convolutional Neural Networks for Lung Nodule Classification , 2015, IPMI.

[308]  Lihua Li,et al.  DEEPre: sequence-based enzyme EC number prediction by deep learning , 2017, Bioinform..

[309]  Eric J Topol,et al.  High-performance medicine: the convergence of human and artificial intelligence , 2019, Nature Medicine.

[310]  Junmo Kim,et al.  Deep Pyramidal Residual Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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

[312]  Marc'Aurelio Ranzato,et al.  Building high-level features using large scale unsupervised learning , 2011, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[313]  Zaher Al Aghbari,et al.  Big data clustering techniques based on Spark: a literature review , 2020, PeerJ Comput. Sci..

[314]  Xiaogang Wang,et al.  Residual Attention Network for Image Classification , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[315]  Bulat Ibragimov,et al.  Deep neural network ensemble for pneumonia localization from a large-scale chest x-ray database , 2019, Comput. Electr. Eng..

[316]  Peter E.D. Love,et al.  Computer vision for behaviour-based safety in construction: A review and future directions , 2020, Adv. Eng. Informatics.

[317]  Richard Bowden,et al.  A Survey of Deep Learning Applications to Autonomous Vehicle Control , 2019, IEEE Transactions on Intelligent Transportation Systems.

[318]  Sridhar Mahadevan,et al.  Imagination Machines: A New Challenge for Artificial Intelligence , 2018, AAAI.

[319]  Eduardo Lleida,et al.  Optimization of the Area Under the ROC Curve using Neural Network Supervectors for Text-Dependent Speaker Verification , 2019, Comput. Speech Lang..

[320]  Thomas Blaschke,et al.  The rise of deep learning in drug discovery. , 2018, Drug discovery today.

[321]  Xuelong Li,et al.  Transfer learning for pedestrian detection , 2013, Neurocomputing.

[322]  Gyanendra K. Verma,et al.  Convolutional neural network: a review of models, methodologies and applications to object detection , 2019, Progress in Artificial Intelligence.

[323]  Tao Lei,et al.  A review of Convolutional-Neural-Network-based action recognition , 2019, Pattern Recognit. Lett..

[324]  Taghi M. Khoshgoftaar,et al.  Survey of review spam detection using machine learning techniques , 2015, Journal of Big Data.

[325]  Bram van Ginneken,et al.  A survey on deep learning in medical image analysis , 2017, Medical Image Anal..

[326]  Hong Hui Tan,et al.  Vanishing Gradient Mitigation with Deep Learning Neural Network Optimization , 2019, 2019 7th International Conference on Smart Computing & Communications (ICSCC).

[327]  Xiang Li,et al.  Deep Learning-Based Image Segmentation on Multimodal Medical Imaging , 2019, IEEE Transactions on Radiation and Plasma Medical Sciences.

[328]  Chen Feng,et al.  DeepMapping: Unsupervised Map Estimation From Multiple Point Clouds , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[329]  Andrew Zisserman,et al.  Spatial Transformer Networks , 2015, NIPS.

[330]  Fei Wang,et al.  Learning from simulation: An end-to-end deep-learning approach for computational ghost imaging. , 2019, Optics express.

[331]  Carlos Guestrin,et al.  "Why Should I Trust You?": Explaining the Predictions of Any Classifier , 2016, ArXiv.

[332]  Shuyuan Yang,et al.  A Survey of Deep Learning-Based Object Detection , 2019, IEEE Access.

[333]  Baskar Ganapathysubramanian,et al.  Interpretable deep learning for guided microstructure-property explorations in photovoltaics , 2018, npj Computational Materials.

[334]  N. Thakur,et al.  Current Trends of Artificial Intelligence for Colorectal Cancer Pathology Image Analysis: A Systematic Review , 2020, Cancers.

[335]  Mikhail S. Gelfand,et al.  A machine learning framework for the prediction of chromatin folding in Drosophila using epigenetic features , 2020, PeerJ Comput. Sci..

[336]  Bianca Zadrozny,et al.  Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers , 2001, ICML.

[337]  Kyunghyun Cho,et al.  QCD-aware recursive neural networks for jet physics , 2017, Journal of High Energy Physics.

[338]  Yu Li,et al.  Deep learning in bioinformatics: Introduction, application, and perspective in the big data era. , 2019, Methods.

[339]  Yasuhiro Fujiwara,et al.  Preventing Gradient Explosions in Gated Recurrent Units , 2017, NIPS.

[340]  Weike Pan,et al.  A survey of transfer learning for collaborative recommendation with auxiliary data , 2016, Neurocomputing.

[341]  Heiko Schwarz,et al.  DeepCABAC: A Universal Compression Algorithm for Deep Neural Networks , 2019, IEEE Journal of Selected Topics in Signal Processing.

[342]  E. Valuations,et al.  A R EVIEW ON E VALUATION M ETRICS F OR D ATA C LASSIFICATION E VALUATIONS , 2015 .

[343]  Saeed Hassanpour,et al.  Deep Learning in Diabetic Foot Ulcers Detection: A Comprehensive Evaluation , 2020, Comput. Biol. Medicine.

[344]  Oscar Cordón,et al.  A comparative study of state-of-the-art evolutionary image registration methods for 3D modeling , 2011, Comput. Vis. Image Underst..

[345]  Mohammed A. Fadhel,et al.  Real-Time Sickle Cell Anemia Diagnosis Based Hardware Accelerator , 2020 .

[346]  Mika S Jain,et al.  Predicting tumour mutational burden from histopathological images using multiscale deep learning , 2020, Nature Machine Intelligence.

[347]  Mohammad-H. Tayarani N.,et al.  Applications of artificial intelligence in battling against covid-19: A literature review , 2020, Chaos, Solitons & Fractals.

[348]  Dimitri Palaz,et al.  End-to-end acoustic modeling using convolutional neural networks for HMM-based automatic speech recognition , 2019, Speech Commun..

[349]  Subhabrata Chakraborti,et al.  A Phase II nonparametric adaptive exponentially weighted moving average control chart , 2016 .

[350]  Begonya Garcia-Zapirain,et al.  Breast Cancer Histopathology Image Classification Using an Ensemble of Deep Learning Models , 2020, Sensors.

[351]  Zili Zhang,et al.  Sample Subset Optimization for Classifying Imbalanced Biological Data , 2011, PAKDD.

[352]  Harm de Vries,et al.  RMSProp and equilibrated adaptive learning rates for non-convex optimization. , 2015 .

[353]  Marc Niethammer,et al.  Quicksilver: Fast predictive image registration – A deep learning approach , 2017, NeuroImage.

[354]  Milos Hauskrecht,et al.  Obtaining Well Calibrated Probabilities Using Bayesian Binning , 2015, AAAI.