A survey on deep learning in medical image analysis

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[38]  Sabee Molloi,et al.  Detecting Cardiovascular Disease from Mammograms With Deep Learning , 2017, IEEE Transactions on Medical Imaging.

[39]  Daniel Forsberg,et al.  Detection and Labeling of Vertebrae in MR Images Using Deep Learning with Clinical Annotations as Training Data , 2017, Journal of Digital Imaging.

[40]  Minsoo Kim,et al.  A Unified Framework for Tumor Proliferation Score Prediction in Breast Histopathology , 2016, DLMIA/ML-CDS@MICCAI.

[41]  Marios Anthimopoulos,et al.  Multi-source Transfer Learning with Convolutional Neural Networks for Lung Pattern Analysis , 2016, IEEE journal of biomedical and health informatics.

[42]  Bram van Ginneken,et al.  Towards automatic pulmonary nodule management in lung cancer screening with deep learning , 2016, Scientific Reports.

[43]  Nico Karssemeijer,et al.  Deep multi-scale location-aware 3D convolutional neural networks for automated detection of lacunes of presumed vascular origin , 2016, NeuroImage: Clinical.

[44]  Elena Marchiori,et al.  Location Sensitive Deep Convolutional Neural Networks for Segmentation of White Matter Hyperintensities , 2016, Scientific Reports.

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[51]  Seyed-Ahmad Ahmadi,et al.  Hough-CNN: Deep learning for segmentation of deep brain regions in MRI and ultrasound , 2016, Comput. Vis. Image Underst..

[52]  Alexander Binder,et al.  Explaining nonlinear classification decisions with deep Taylor decomposition , 2015, Pattern Recognit..

[53]  Ronald M. Summers,et al.  A Bottom-Up Approach for Pancreas Segmentation Using Cascaded Superpixels and (Deep) Image Patch Labeling , 2015, IEEE Transactions on Image Processing.

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

[55]  Lei Wang,et al.  HEp-2 Cell Image Classification With Deep Convolutional Neural Networks , 2015, IEEE Journal of Biomedical and Health Informatics.

[56]  Fei-Fei Li,et al.  Deep visual-semantic alignments for generating image descriptions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[57]  Guang-Zhong Yang,et al.  Deep Learning for Health Informatics , 2017, IEEE Journal of Biomedical and Health Informatics.

[58]  Nico Karssemeijer,et al.  Large scale deep learning for computer aided detection of mammographic lesions , 2017, Medical Image Anal..

[59]  Qi Dou.  Multi-level Contextual 3D CNNs for False Positive Reduction in Pulmonary Nodule Detection , 2017 .

[60]  Xudong Jiang,et al.  Accurate Cervical Cell Segmentation from Overlapping Clumps in Pap Smear Images , 2017, IEEE Trans. Medical Imaging.

[61]  Jinlian Ma,et al.  A pre‐trained convolutional neural network based method for thyroid nodule diagnosis , 2017, Ultrasonics.

[62]  Qianjin Feng,et al.  Cascade of multi-scale convolutional neural networks for bone suppression of chest radiographs in gradient domain , 2017, Medical Image Anal..

[63]  Joachim M. Buhmann,et al.  Glaucoma detection using entropy sampling and ensemble learning for automatic optic cup and disc segmentation , 2017, Comput. Medical Imaging Graph..

[64]  Gustavo Carneiro,et al.  Combining deep learning and level set for the automated segmentation of the left ventricle of the heart from cardiac cine magnetic resonance , 2017, Medical Image Anal..

[65]  Petia Radeva,et al.  A Convolutional Neural Network for Automatic Characterization of Plaque Composition in Carotid Ultrasound , 2017, IEEE Journal of Biomedical and Health Informatics.

[66]  Carmen C. Y. Poon,et al.  Automatic Detection and Classification of Colorectal Polyps by Transferring Low-Level CNN Features From Nonmedical Domain , 2017, IEEE Journal of Biomedical and Health Informatics.

[67]  Jie-Zhi Cheng,et al.  Accurate Cervical Cell Segmentation from Overlapping Clumps in Pap Smear Images , 2017, IEEE Transactions on Medical Imaging.

[68]  Jianwei Zhao,et al.  Automatic detection and classification of leukocytes using convolutional neural networks , 2016, Medical & Biological Engineering & Computing.

[69]  Wei Li,et al.  Pulmonary Nodule Classification with Deep Convolutional Neural Networks on Computed Tomography Images , 2016, Comput. Math. Methods Medicine.

[70]  Subhashini Venugopalan,et al.  Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. , 2016, JAMA.

[71]  J. Sato,et al.  Using deep belief network modelling to characterize differences in brain morphometry in schizophrenia , 2016, Scientific Reports.

[72]  Hui Feng,et al.  CNN-SVM for Microvascular Morphological Type Recognition with Data Augmentation , 2016, Journal of Medical and Biological Engineering.

[73]  Lubomir M. Hadjiiski,et al.  Mass detection in digital breast tomosynthesis: Deep convolutional neural network with transfer learning from mammography. , 2016, Medical physics.

[74]  Lubomir M. Hadjiiski,et al.  Bladder Cancer Segmentation in CT for Treatment Response Assessment: Application of Deep-Learning Convolution Neural Network—A Pilot Study , 2016, Tomography.

[75]  Kyong Hwan Jin,et al.  Fast and robust segmentation of the striatum using deep convolutional neural networks , 2016, Journal of Neuroscience Methods.

[76]  Max A. Viergever,et al.  Automatic coronary artery calcium scoring in cardiac CT angiography using paired convolutional neural networks , 2016, Medical Image Anal..

[77]  Qi Zhang,et al.  Deep learning based classification of breast tumors with shear-wave elastography. , 2016, Ultrasonics.

[78]  Sven Loncaric,et al.  Detection of exudates in fundus photographs using deep neural networks and anatomical landmark detection fusion , 2016, Comput. Methods Programs Biomed..

[79]  Feng Chen,et al.  Automatic abdominal multi-organ segmentation using deep convolutional neural network and time-implicit level sets , 2016, International Journal of Computer Assisted Radiology and Surgery.

[80]  Jialin Peng,et al.  Automatic 3D liver segmentation based on deep learning and globally optimized surface evolution , 2016, Physics in medicine and biology.

[81]  Dinggang Shen,et al.  Convolutional Neural Network for Reconstruction of 7T-like Images from 3T MRI Using Appearance and Anatomical Features , 2016, LABELS/DLMIA@MICCAI.

[82]  Pavel Kisilev,et al.  Medical Image Description Using Multi-task-loss CNN , 2016, LABELS/DLMIA@MICCAI.

[83]  Yaozong Gao,et al.  Estimating CT Image from MRI Data Using 3D Fully Convolutional Networks , 2016, LABELS/DLMIA@MICCAI.

[84]  Sotirios A. Tsaftaris,et al.  Whole Image Synthesis Using a Deep Encoder-Decoder Network , 2016, SASHIMI@MICCAI.

[85]  Alejandro F. Frangi,et al.  Automated Quality Assessment of Cardiac MR Images Using Convolutional Neural Networks , 2016, SASHIMI@MICCAI.

[86]  Hayit Greenspan,et al.  Longitudinal Multiple Sclerosis Lesion Segmentation Using Multi-view Convolutional Neural Networks , 2016, LABELS/DLMIA@MICCAI.

[87]  Edmund Koch,et al.  Learning Thermal Process Representations for Intraoperative Analysis of Cortical Perfusion During Ischemic Strokes , 2016, LABELS/DLMIA@MICCAI.

[88]  Song Wang,et al.  Three-Dimensional CT Image Segmentation by Combining 2D Fully Convolutional Network with 3D Majority Voting , 2016, LABELS/DLMIA@MICCAI.

[89]  Max A. Viergever,et al.  Automatic Slice Identification in 3D Medical Images with a ConvNet Regressor , 2016, LABELS/DLMIA@MICCAI.

[90]  Leonid Karlinsky,et al.  A Region Based Convolutional Network for Tumor Detection and Classification in Breast Mammography , 2016, LABELS/DLMIA@MICCAI.

[91]  Yen-Wei Chen,et al.  HEp-2 Cell Classification Using K-Support Spatial Pooling in Deep CNNs , 2016, LABELS/DLMIA@MICCAI.

[92]  Lisa Tang,et al.  Deep Learning of Brain Lesion Patterns for Predicting Future Disease Activity in Patients with Early Symptoms of Multiple Sclerosis , 2016, LABELS/DLMIA@MICCAI.

[93]  Yoni Donner,et al.  Fully Automating Graf's Method for DDH Diagnosis Using Deep Convolutional Neural Networks , 2016, LABELS/DLMIA@MICCAI.

[94]  P. Cattin,et al.  Multi-dimensional Gated Recurrent Units for the Segmentation of Biomedical 3D-Data , 2016, LABELS/DLMIA@MICCAI.

[95]  Tammy Riklin-Raviv,et al.  De-noising of Contrast-Enhanced MRI Sequences by an Ensemble of Expert Deep Neural Networks , 2016, LABELS/DLMIA@MICCAI.

[96]  Erik Smistad,et al.  Vessel Detection in Ultrasound Images Using Deep Convolutional Neural Networks , 2016, LABELS/DLMIA@MICCAI.

[97]  Hayit Greenspan,et al.  Fully Convolutional Network for Liver Segmentation and Lesions Detection , 2016, LABELS/DLMIA@MICCAI.

[98]  Daguang Xu,et al.  Robust 3D Organ Localization with Dual Learning Architectures and Fusion , 2016, LABELS/DLMIA@MICCAI.

[99]  Hariharan Ravishankar,et al.  Understanding the Mechanisms of Deep Transfer Learning for Medical Images , 2016, LABELS/DLMIA@MICCAI.

[100]  Gabriel J. Brostow,et al.  Automated Retinopathy of Prematurity Case Detection with Convolutional Neural Networks , 2016, LABELS/DLMIA@MICCAI.

[101]  Juho Kannala,et al.  Cell Segmentation Proposal Network for Microscopy Image Analysis , 2016, LABELS/DLMIA@MICCAI.

[102]  Konstantinos Kamnitsas,et al.  Real-Time Standard Scan Plane Detection and Localisation in Fetal Ultrasound Using Fully Convolutional Neural Networks , 2016, MICCAI.

[103]  Max A. Viergever,et al.  Deep Learning for Multi-Task Medical Image Segmentation in Multiple Modalities , 2016, MICCAI.

[104]  Andrew Zisserman,et al.  SpineNet: Automatically Pinpointing Classification Evidence in Spinal MRIs , 2016, MICCAI.

[105]  Le Lu,et al.  Pancreas Segmentation in MRI Using Graph-Based Decision Fusion on Convolutional Neural Networks , 2016, MICCAI.

[106]  Shaoting Zhang,et al.  Recognizing End-Diastole and End-Systole Frames via Deep Temporal Regression Network , 2016, MICCAI.

[107]  Lin Yang,et al.  3D Segmentation of Glial Cells Using Fully Convolutional Networks and k-Terminal Cut , 2016, MICCAI.

[108]  Junzhou Huang,et al.  Subtype Cell Detection with an Accelerated Deep Convolution Neural Network , 2016, MICCAI.

[109]  Ronald M. Summers,et al.  Multi-label Deep Regression and Unordered Pooling for Holistic Interstitial Lung Disease Pattern Detection , 2016, MLMI@MICCAI.

[110]  Gustavo Carneiro,et al.  The Automated Learning of Deep Features for Breast Mass Classification from Mammograms , 2016, MICCAI.

[111]  Lisheng Wang,et al.  Deep Fusion Net for Multi-atlas Segmentation: Application to Cardiac MR Images , 2016, MICCAI.

[112]  Vincent Lepetit,et al.  Automated Age Estimation from Hand MRI Volumes Using Deep Learning , 2016, MICCAI.

[113]  Dorin Comaniciu,et al.  An Artificial Agent for Anatomical Landmark Detection in Medical Images , 2016, MICCAI.

[114]  Junzhou Huang,et al.  Detecting 10, 000 Cells in One Second , 2016, MICCAI.

[115]  Paolo Zaffino,et al.  Deep Neural Networks for Fast Segmentation of 3D Medical Images , 2016, MICCAI.

[116]  Dwarikanath Mahapatra,et al.  Retinal Image Quality Classification Using Saliency Maps and CNNs , 2016, MLMI@MICCAI.

[117]  Wei Shen,et al.  Learning from Experts: Developing Transferable Deep Features for Patient-Level Lung Cancer Prediction , 2016, MICCAI.

[118]  Bostjan Likar,et al.  Model-Based Segmentation of Vertebral Bodies from MR Images with 3D CNNs , 2016, MICCAI.

[119]  Junzhou Huang,et al.  Imaging Biomarker Discovery for Lung Cancer Survival Prediction , 2016, MICCAI.

[120]  Danny Ziyi Chen,et al.  A Deep Learning Approach for Semantic Segmentation in Histology Tissue Images , 2016, MICCAI.

[121]  Stephen Lin,et al.  DeepVessel: Retinal Vessel Segmentation via Deep Learning and Conditional Random Field , 2016, MICCAI.

[122]  Dinggang Shen,et al.  Deep Ensemble Sparse Regression Network for Alzheimer's Disease Diagnosis , 2016, MLMI@MICCAI.

[123]  Lin Yang,et al.  Spatial Clockwork Recurrent Neural Network for Muscle Perimysium Segmentation , 2016, MICCAI.

[124]  Zhaozheng Yin,et al.  A Hierarchical Convolutional Neural Network for Mitosis Detection in Phase-Contrast Microscopy Images , 2016, MICCAI.

[125]  Konstantinos Kamnitsas,et al.  Fast Fully Automatic Segmentation of the Human Placenta from Motion Corrupted MRI , 2016, MICCAI.

[126]  Ronald M. Summers,et al.  Automatic Lymph Node Cluster Segmentation Using Holistically-Nested Neural Networks and Structured Optimization in CT Images , 2016, MICCAI.

[127]  Yefeng Zheng,et al.  Coronary Centerline Extraction via Optimal Flow Paths and CNN Path Pruning , 2016, MICCAI.

[128]  Horst Bischof,et al.  Regressing Heatmaps for Multiple Landmark Localization Using CNNs , 2016, MICCAI.

[129]  Dinggang Shen,et al.  3D Deep Learning for Multi-modal Imaging-Guided Survival Time Prediction of Brain Tumor Patients , 2016, MICCAI.

[130]  Konstantinos Kamnitsas,et al.  Multi-input Cardiac Image Super-Resolution Using Convolutional Neural Networks , 2016, MICCAI.

[131]  Tao Xu,et al.  Multimodal Deep Learning for Cervical Dysplasia Diagnosis , 2016, MICCAI.

[132]  Ghassan Hamarneh,et al.  Topology Aware Fully Convolutional Networks for Histology Gland Segmentation , 2016, MICCAI.

[133]  Ghassan Hamarneh,et al.  Multi-resolution-Tract CNN with Hybrid Pretrained and Skin-Lesion Trained Layers , 2016, MLMI@MICCAI.

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

[135]  Seyed-Ahmad Ahmadi,et al.  Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields , 2016, MICCAI.

[136]  Hadi Rezaeilouyeh,et al.  Microscopic medical image classification framework via deep learning and shearlet transform , 2016, Journal of medical imaging.

[137]  M. Abràmoff,et al.  Improved Automated Detection of Diabetic Retinopathy on a Publicly Available Dataset Through Integration of Deep Learning. , 2016, Investigative ophthalmology & visual science.

[138]  Jaime S. Cardoso,et al.  Deep Learning and Data Labeling for Medical Applications , 2016, Lecture Notes in Computer Science.

[139]  Nikos Komodakis,et al.  A Deep Metric for Multimodal Registration , 2016, MICCAI.

[140]  Ge Wang,et al.  A Perspective on Deep Imaging , 2016, IEEE Access.

[141]  Joseph Antony,et al.  Quantifying radiographic knee osteoarthritis severity using deep convolutional neural networks , 2016, 2016 23rd International Conference on Pattern Recognition (ICPR).

[142]  Lin Yang,et al.  Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation , 2016, NIPS.

[143]  Eduardo Valle,et al.  Towards Automated Melanoma Screening: Exploring Transfer Learning Schemes , 2016, ArXiv.

[144]  Luc Van Gool,et al.  Deep Retinal Image Understanding , 2016, MICCAI.

[145]  Andrés Ortiz,et al.  Ensembles of Deep Learning Architectures for the Early Diagnosis of the Alzheimer's Disease , 2016, Int. J. Neural Syst..

[146]  Hao Chen,et al.  VoxResNet: Deep Voxelwise Residual Networks for Volumetric Brain Segmentation , 2016, ArXiv.

[147]  Christopher Joseph Pal,et al.  The Importance of Skip Connections in Biomedical Image Segmentation , 2016, LABELS/DLMIA@MICCAI.

[148]  Pablo Lamata,et al.  Recurrent Fully Convolutional Neural Networks for Multi-slice MRI Cardiac Segmentation , 2016, RAMBO+HVSMR@MICCAI.

[149]  John A. Quinn,et al.  Deep Convolutional Neural Networks for Microscopy-Based Point of Care Diagnostics , 2016, MLHC.

[150]  Bin Yan,et al.  Image Prediction for Limited-angle Tomography via Deep Learning with Convolutional Neural Network , 2016, ArXiv.

[151]  Mohammad Havaei,et al.  HeMIS: Hetero-Modal Image Segmentation , 2016, MICCAI.

[152]  Nassir Navab,et al.  Deep Active Contours , 2016, ArXiv.

[153]  Yang Li,et al.  Gland Instance Segmentation by Deep Multichannel Side Supervision , 2016, MICCAI.

[154]  Xiao Yang,et al.  Fast Predictive Image Registration , 2016, LABELS/DLMIA@MICCAI.

[155]  Hayit Greenspan,et al.  Visualizing and enhancing a deep learning framework using patients age and gender for chest x-ray image retrieval , 2016, SPIE Medical Imaging.

[156]  Hao Chen,et al.  Iterative Multi-domain Regularized Deep Learning for Anatomical Structure Detection and Segmentation from Ultrasound Images , 2016, MICCAI.

[157]  Hao Chen,et al.  3D Deeply Supervised Network for Automatic Liver Segmentation from CT Volumes , 2016, MICCAI.

[158]  Ayman El-Baz,et al.  Alzheimer's Disease Diagnostics by a Deeply Supervised Adaptable 3D Convolutional Network , 2016, ArXiv.

[159]  Shuo Li,et al.  Multi-modal vertebrae recognition using Transformed Deep Convolution Network , 2016, Comput. Medical Imaging Graph..

[160]  Hui Li,et al.  Digital mammographic tumor classification using transfer learning from deep convolutional neural networks , 2016, Journal of medical imaging.

[161]  Sven Loncaric,et al.  Segmentation of the foveal microvasculature using deep learning networks , 2016, Journal of biomedical optics.

[162]  Germain Forestier,et al.  Detection of lobular structures in normal breast tissue , 2016, Comput. Biol. Medicine.

[163]  Ronald M. Summers,et al.  Spatial Aggregation of Holistically-Nested Networks for Automated Pancreas Segmentation , 2016, MICCAI.

[164]  Thomas Brox,et al.  3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation , 2016, MICCAI.

[165]  Mitko Veta,et al.  Cutting Out the Middleman: Measuring Nuclear Area in Histopathology Slides Without Segmentation , 2016, MICCAI.

[166]  Dayong Wang,et al.  Deep Learning for Identifying Metastatic Breast Cancer , 2016, ArXiv.

[167]  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).

[168]  Kullervo Hynynen,et al.  Deep Learning Convolutional Networks for Multiphoton Microscopy Vasculature Segmentation , 2016, ArXiv.

[169]  Amir Alansary,et al.  Learning under Distributed Weak Supervision , 2016, ArXiv.

[170]  Stefan Bauer,et al.  Multi-Organ Cancer Classification and Survival Analysis , 2016, ArXiv.

[171]  Hiroshi Fujita,et al.  Automated detection of pulmonary nodules in PET/CT images: Ensemble false-positive reduction using a convolutional neural network technique. , 2016, Medical physics.

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[173]  Anant Madabhushi,et al.  A Deep Convolutional Neural Network for segmenting and classifying epithelial and stromal regions in histopathological images , 2016, Neurocomputing.

[174]  David J. Kriegman,et al.  Dense Volume-to-Volume Vascular Boundary Detection , 2016, MICCAI.

[175]  B. van Ginneken,et al.  Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis , 2016, Scientific Reports.

[176]  Fang Lu,et al.  Automatic 3D liver location and segmentation via convolutional neural network and graph cut , 2016, International Journal of Computer Assisted Radiology and Surgery.

[177]  Ronald M. Summers,et al.  Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique , 2016 .

[178]  Hamid R. Tizhoosh,et al.  Generating binary tags for fast medical image retrieval based on convolutional nets and Radon Transform , 2016, 2016 International Joint Conference on Neural Networks (IJCNN).

[179]  D. Shen,et al.  Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans , 2016, Scientific Reports.

[180]  Philippe Burlina,et al.  Detection of age-related macular degeneration via deep learning , 2016, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI).

[181]  Yaozong Gao,et al.  Fully convolutional networks for multi-modality isointense infant brain image segmentation , 2016, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI).

[182]  Nico Karssemeijer,et al.  Non-uniform patch sampling with deep convolutional neural networks for white matter hyperintensity segmentation , 2016, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI).

[183]  David Dagan Feng,et al.  Transfer learning of a convolutional neural network for HEp-2 cell image classification , 2016, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI).

[184]  Nassir Navab,et al.  Structure-based assessment of cancerous mitochondria using deep networks , 2016, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI).

[185]  Heng Huang,et al.  Latent source mining in FMRI data via deep neural network , 2016, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI).

[186]  Max A. Viergever,et al.  Automatic segmentation of the left ventricle in cardiac CT angiography using convolutional neural networks , 2016, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI).

[187]  Rahil Garnavi,et al.  Classification of dermoscopy patterns using deep convolutional neural networks , 2016, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI).

[188]  Huazhu Fu,et al.  Retinal vessel segmentation via deep learning network and fully-connected conditional random fields , 2016, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI).

[189]  Mingchen Gao,et al.  Deep vessel tracking: A generalized probabilistic approach via deep learning , 2016, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI).

[190]  Hongzhi Wang,et al.  A hybrid learning approach for semantic labeling of cardiac CT slices and recognition of body position , 2016, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI).

[191]  Nitin Singhal,et al.  Hybrid approach for automatic segmentation of fetal abdomen from ultrasound images using deep learning , 2016, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI).

[192]  Hao Chen,et al.  Automated mitosis detection with deep regression networks , 2016, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI).

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