US 9739783B1
· Kumar et al.
· 2017
[cited by applicant]
US 9811906B1
· Vizitiu et al.
· 2017
[cited by applicant]
US 9947102B2
· Xu et al.
· 2018
[cited by applicant]
US 9965863B2
· Xu et al.
· 2018
[cited by applicant]
CN 109685809
· 2019
[cited by applicant]
EP 3252671
· 2016
[cited by applicant]
EP 3392832
· 2018
[cited by applicant]
WO WO2016182551
· 2016
[cited by applicant]
WO WO2017210690
· 2017
[cited by applicant]
WO WO2018015080
· 2018
[cited by applicant]
WO WO2018015414
· 2018
[cited by applicant]
WO WO2019005722
· 2019
[cited by applicant]
WO WO2019005722A1
· 2019
[cited by examiner]
Extended Search Report in European Appln. No. 20794854.8, dated Apr. 19, 2023, 5 pages.
[cited by applicant]
Hu et al. “Brain Tumor Segmentation Using Multi-Cascaded Convolutional Neural Networks and Conditional Random Field,” IEEE Access, Jul. 8, 2019, 7:92615-92629.
[cited by applicant]
Wang et al., “Abdominal Multi-organ Segmentation with Organ-Attention Networks and Statistical Fusion,” CoRR, submitted on Apr. 23, 2018, arXiv:1804.08414v1, 21 pages.
[cited by applicant]
Wang et al., English translation of CN 109685809 A, published on Apr. 26, 2019, 14 pages.
[cited by applicant]
Arthur et al., “k-means++: The advantages of careful seeding,” Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, Jan. 2007, 1027-1035.
[cited by applicant]
Banerjee et al., “Clustering on the unit hypersphere using von mises-fisher distributions,” Journal of Machine Learning Research, Sep. 2005, 6:1345-1382.
[cited by applicant]
Bienenstock et al., “Compositionality, MDL Priors, and Object Recognition,” Proceedings of the 9th International Conference on Neural Information Processing Systems, Dec. 1996, 838-844.
[cited by applicant]
Cai et al., “Cascade R-CNN: Delving into High Quality Object Detection,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2018, 6154-6162.
[cited by applicant]
Carlson et al., “A Sparse Object Coding Scheme in Area V4,” Current Biology, Feb. 22, 2011, 21(4):288-293.
[cited by applicant]
Dai et al., “Unsupervised learning of dictionaries of hierarchical compositional models,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Jun. 2014, 2505-2512.
[cited by applicant]
Deng et al., “ImageNet: A Large-Scale Hierarchical Image Database,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Jun. 2009, 248-255.
[cited by applicant]
DeVries et al., “Improved regularization of convolutional neural networks with cutout,” CoRR, submitted on Nov. 29, 2017, arXiv:1708.04552v2, 8 pages.
[cited by applicant]
Fawzi et al., “Measuring the effect of nuisance variables on classifiers,” Proceedings of the British Machine Vision Conference (BMVC), Sep. 2016, 12 pages.
[cited by applicant]
Fidler et al., “Learning a hierarchical compositional shape vocabulary for multiclass object representation,” CoRR, submitted on Aug. 23, 2014, arXiv:1408.5516v1, 17 pages.
[cited by applicant]
Fodor et al., “Connectionism and cognitive architecture: A critical analysis,” Cognition, Mar. 1988, 28(1-2):3-71.
[cited by applicant]
George et al., “A generative vision model that trains with high data efficiency and breaks text-based captchas,” Science, Oct. 26, 2017, 358(6368):eaag2612, 19 pages.
[cited by applicant]
Girshick et al., “Rich feature hierarchies for accurate object detection and semantic segmentation,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2014, 580-587.
[cited by applicant]
Girshick, “Fast R-CNN,” Proceedings of the IEEE International Conference on Computer Vision (ICCV), Dec. 2015, 1440-1448.
[cited by applicant]
International Preliminary Report on Patentability in International Appln. No. PCT/US2022/033438, mailed on Dec. 28, 2023, 9 pages.
[cited by applicant]
International Search Report and Written Opinion in International Appln. No. PCT/US2022/033438, mailed on Oct. 20, 2022, 10 pages.
[cited by applicant]
Jin et al., “Context and hierarchy in a probabilistic image model,” Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06), Jun. 2006, 2145-2152.
[cited by applicant]
Kingma et al., “Adam: A method for stochastic optimization,” CoRR, submitted on Dec. 22, 2014, arXiv:1412.6980v1, 9 pages.
[cited by applicant]
Kortylewski et al., “Compositional Convolutional Networks For Robust Object Classification under Occlusion,” CoRR, submitted on May 29, 2019, arXiv:1905.11826v2, 12 pages.
[cited by applicant]
Kortylewski et al., “Greedy structure learning of hierarchical compositional models,” CoRR, submitted on Jan. 22, 2017, arXiv:1701.06171v1, 16 pages.
[cited by applicant]
Kortylewski et al., “Probabilistic compositional active basis models for robust pattern recognition,” Proceedings of the British Machine Vision Conference (BMVC), Sep. 2016, 12 pages.
[cited by applicant]
Kortylewski, “Model-based image analysis for forensic shoe print recognition,” Dissertation for the Degree of Doctor of Philosophy, University of Basel, Jun. 2017, 124 pages.
[cited by applicant]
Lampert et al., “Beyond sliding windows: Object localization by efficient subwindow search,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2008, 8 pages.
[cited by applicant]
Li et al., “AOGNets: Compositional Grammatical Architectures for Deep Learning,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2019, 6220-6230.
[cited by applicant]
Li et al., “SymmNet: A Symmetric Convolutional Neural Network for Occlusion Detection,” Proceedings of the British Machine Vision Conference (BMVC), Sep. 2018, 14 pages.
[cited by applicant]
Liao et al., “Learning deep parsimonious representations,” Advances in Neural Information Processing Systems, Dec. 2016, 29:5076-5084.
[cited by applicant]
Lin et al., “Microsoft COCO: Common Objects in Context,” Computer Vision—ECCV 2014 (LNCS), Sep. 2014, 8693:740-755.
[cited by applicant]
Reddy et al., “Occlusion-Net: 2D/3D Occluded Keypoint Localization Using Graph Networks,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2019, 7326-7335.
[cited by applicant]
Ren et al., “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks,” Advances in Neural Information Processing Systems, Dec. 2015, vol. 28, 9 pages.
[cited by applicant]
Roe et al., “Toward a Unified Theory of Visual Area V4,” Neuron, Apr. 12, 2012, 74(1):12-29.
[cited by applicant]
Sasikumar et al., “First-pass processing of value cues in the ventral visual pathway,” Current Biology, Feb. 8, 2018, 28(4):538-548.
[cited by applicant]
Simonyan et al., “Very deep convolutional networks for large-scale image recognition,” CoRR, submitted on Dec. 23, 2014, arXiv:1409.1556v5, 13 pages.
[cited by applicant]
Stone et al., “Teaching Compositionality to CNNs,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Jul. 2017, 5058-5067.
[cited by applicant]
Sun et al., “Symmetric stereo matching for occlusion handling,” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2005, 399-406.
[cited by applicant]
Tabernik et al., “Towards deep compositional networks,” Proceedings of the 23rd International Conference on Pattern Recognition (ICPR), Dec. 2016, 3470-3475.
[cited by applicant]
Tang et al., “Deeply Learned Compositional Models for Human Pose Estimation,” Computer Vision—ECCV 2018 (LNCS), Sep. 2018, 11207:190-206.
[cited by applicant]
Tang et al., “Towards a unified compositional model for visual pattern modeling,” Proceedings of the IEEE International Conference on Computer Vision (ICCV), Oct. 2017, 2784-2793.
[cited by applicant]
The Handbook of Brain Theory and Neural Networks, 2nd ed., Arbib (ed.), 2003, pp. 244-248.
[cited by applicant]
Vaziri et al., “A channel for 3d environmental shape in anterior inferotemporal cortex,” Neuron, Oct. 2014, 84(1):55-62.
[cited by applicant]
Wang et al., “Detecting semantic parts on partially occluded objects,” Proceedings of the British Machine Vision Conference (BMVC), Sep. 2017, 13 pages.
[cited by applicant]
Wang et al., “Discovering Internal Representations from Object-CNNs Using Population Encoding,” CoRR, submitted on Nov. 21, 2015, arXiv:1511.06855v1, 12 pages.
[cited by applicant]
Wang et al., “Visual concepts and compositional voting,” CoRR, submitted on Nov. 13, 2017, arXiv:1711.04451v1, 37 pages.
[cited by applicant]
Xiang et al., “Beyond Pascal: A benchmark for 3D object detection in the wild,” IEEE Winter Conference on Applications of Computer Vision, Mar. 2014, 75-82.
[cited by applicant]
Xiang et al., “Object Detection by 3D Aspectlets and Occlusion Reasoning, ” IEEE International Conference on Computer Vision Workshops, Dec. 2013, 530-537.
[cited by applicant]
Xiao et al., “Tdapnet: Prototype network with recurrent top-down attention for robust object classification under partial occlusion,” CoRR, submitted on Sep. 9, 2019, arXiv:1909,03879v1, 11 pages.
[cited by applicant]
Yamane et al., “A neural code for three-dimensional object shape in macaque inferotemporal cortex,” Nature Neuroscience, Oct. 5, 2008, 11(11):1352-1360.
[cited by applicant]
Yan et al., “Inferring occluded features for fast object detection,” Signal Processing, May 2015, 110:188-198.
[cited by applicant]
Yun et al., “CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features,” CoRR, submitted on May 13, 2019, arXiv:1905.04899v1, 14 pages.
[cited by applicant]
Zhang et al., “Deepvoting: A robust and explainable deep network for semantic part detection under partial occlusion,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2018,…
[cited by applicant]
Zhang et al., “Interpretable convolutional neural networks,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2018, 8827-8836.
[cited by applicant]
Zhang et al., “Occlusion-aware R-CNN: Detecting Pedestrians in a Crowd,” CORR, submitted on Jul. 23, 2018, arXiv:1807.08407v1, 17 pages.
[cited by applicant]
Zhu et al., “Robustness of Object Recognition under Extreme Occlusion in Humans and Computational Models,” Proceedings of the 41st Annual Meeting of the Cognitive Science Society: Creativity + Cognition + Computation, J…
[cited by applicant]
Zhu et al., “Unsupervised Structure Learning: Hierarchical Recursive Composition, Suspicious Coincidence and Competitive Exclusion,” Computer Vision—ECCV 2008 (LNCS), Oct. 2008, 5303:759-773.
[cited by applicant]
Abraham et al., “Extracting Brain Regions from Rest fMRI with Total-Variation Constrained Dictionary Learning,” MICCAI., 2013, 16:2:607-15.
[cited by applicant]
Ansari et al. “Role of Magnetic Resonance Imaging in the Detection and Characterization of Solid Pancreatic Nodules: An Update.” World Journal of Radiology, 28, 7(11), Nov. 2015, 361-374.
[cited by applicant]
Asman et al., “Formulating spatially varying performance in the statistical fusion framework,” IEEE Trans. On Med, Imaging, 2012, 31:6:1326-1336.
[cited by applicant]
Asman et al., “Non-local statistical label fusion for multi-atlas segmentation,” Med. Image Anal., Feb. 2013, 17:2:194-208.
[cited by applicant]
Aydogan et al., “Analysis of Trabecular Bone Microstructure Using Contour Tree Connectivity, ” MICCAI., 2013, 16:2:428-35.
[cited by applicant]
Bieth et al., “Atlas Construction for Dynamic (4D) Pet Using Diffeomorphic Transformations, ” MICCAI, 2013, 35-42.
[cited by applicant]
Boykov et al., “Fast approximate energy minimization via graph cuts,” IEEE Transactions on PAMI, 2001, 23:11:122-1239.
[cited by applicant]
Brosch et al., “Manifold Learning of Brain MRIs by Deep Learning,” MICCAI., 2013, 16:2:633-40.
[cited by applicant]
Cerrolaza et al., “Multiresolution Hierarchical Shape Models in 3D Subcortical Brain Structures, ” MICCAI., 2013, 16:2:641-8.
[cited by applicant]
Chang et al., “Characterization of Tissue Histopathology via Predictive Sparse Decomposition and Spatial Pyramid Matching,” MICCAI, 2013, 8150:91-98.
[cited by applicant]
Chatelain et al., “Learning from Multiple Experts with Random Forests: Application to the Segmentation of the Midbrain in 3D Ultrasound,” MICCAI, 2013, 16:2:230-7.
[cited by applicant]
Chen et al., “Attention to scale: Scale-aware semantic image segmentation,” In proceedings of the IEEE conf. on CCVPR, 2016, 3640-3649.
[cited by applicant]
Chen et al., “DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 40(4): 834-848.
[cited by applicant]
Chen et al., “VoxResNet:Deep voxelwise residual networks for brain segmentation from 3D MR images, ” NeuroImage, 2018, 170:446-455.
[cited by applicant]
Chu et al., “Multi- organ segmentation based on spatially-divided probabilistic atlas from 3D abdominal CT images,” Lecture Notes in Comp. Sci., 2013, 8150LNCS(part 2) 165-172.
[cited by applicant]
Ciompi et al., “Stent Shape Estimation through a Comprehensive Interpretation of Intravascular Ultrasound Images, ” MICCAI, 2013, 16:2:345-52.
[cited by applicant]
Ciresan et al., “Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks,” MICCAI., 2013, 16:2:411-8.
[cited by applicant]
Cisek et al., “3D U-Net: Learning dense volumetric segmentation from sparse annotation,” MICCAI, 2016, 424-432.
[cited by applicant]
Cruz-Roa et al., “A Deep Learning Architecture for Image Representation, Visual Interpretability and Automated Basal-Cell Carcinoma Cancer Detection,” MICCAI., 2013, 16:2:403-10.
[cited by applicant]
Da Mota et al., “Enhancing the Reproducibility of Group Analysis with Randomized Brain Parcellations, ” MICCAI., 2013, 16:2:591-8.
[cited by applicant]
Datar et al., “Geodesic Distances to Landmarks for Dense Correspondence on Ensembles of Complex Shapes,” MICCAI., 2013, 16:19-26.
[cited by applicant]
De Silva et al., “Improving 2D-3D Registration Optimization Using Learned Prostate Motion Data,” MICCAI, 2013, 16:2:124-31.
[cited by applicant]
Dinse et al., “A Histology-Based Model of Quantitative T1 Contrast for In-vivo Cortical Parcellation of High-Resolution 7 Tesla Brain MR Images,”.
[cited by applicant]
Dou et al., “3D deeply supervised network for automatic liver segmentation from CT volumes,” Med. Image Analysis, Oct. 2017, 41:40-54.
[cited by applicant]
Dou et al., “Multilevel Contextual 3-D CNNs for False Positive Reduction in Pulmonary Nodule Detection”, IEEE Tbe, 2017, 64(7): 1558-1567.
[cited by applicant]
Durichen et al., “Respiratory Motion Compensation with Relevance Vector Machines,” MICCAI., 2013, 108-115.
[cited by applicant]
Everingham et al., The PASCAL visual object classes challenge 2012 (VOC2012) results. 2012, Pascal2, 24 pages.
[cited by applicant]
Farag et al., “Automatic pancreas segmentation using coarse to-fine superpixel labeling,” Deep learning ans Convutional Neural Networks for Medical Computing, 2017.
[cited by applicant]
Forman et al., “Free-Breathing Whole-Heart Coronary MRA: Motion Compensation Integrated into 3D Cartesian Compressed Sensing Reconstruction,” MICCAI., 2013, 16:2:575-82.
[cited by applicant]
Gao et al., “Incremental Learning with Selective Memory (ILSM): Towards Fast Prostate Localization for Image Guided Radiotherapy,” MICCAI., 2013, 16:2:378-386.
[cited by applicant]
Gibson et al., “Automatic multi-organ segmentation on abdominal CT with dense V- networks,” IEEE Trans. Med. Imaging, Aug. 2018, 37:8:1822-1834.
[cited by applicant]
Ginsburg et al., “Variable Importance in Nonlinear Kernels (VINK): Classification of Digitized Histopathology,” MICCAI., 2013, 16:2:238-45.
[cited by applicant]
Glocker et al., “Vertebrae Localization in Pathological Spine CT via Dense Classification from Sparse Annotations,” MICCAI., 2013, 16:2:262-70.
[cited by applicant]
Gomez et al., “3D Intraventricular Flow Mapping from Colour Doppler Images and Wall Motion,” MICCAI,2013, 16:2:476-83.
[cited by applicant]
Grbic et al., “Image-Based Computational Models for TAVI Planning: From CT Images to Implant Deployment,” MICCAI., 2013, 16:2:395-402.
[cited by applicant]
Hacihaliloglu e et al., “Statistical Shape Model to 3D Ultrasound Registration for Spine Interventions Using Enhanced Local Phase Features,” MICCAI., 2013, 16:2:361-8.
[cited by applicant]
Hamy et al., “Respiratory Motion Correction in Dynamic-MRI: Application to Small Bowel Motility Quantification during Free Breathing,” MICCAI, 2013, 16:2:132-40.
[cited by applicant]
Havaei et al., “Brain tumor segmenation with deep neural networks,” Medical Image analysis, Jan. 2017, 35:18-31.
[cited by applicant]
He et al., “Deep Residual Learning for Image Recognition”, 2016, CVPR, 770-778.
[cited by applicant]
Heimann et al., “Comparison and evaluation of methods for live segmentation from CT datasets,” IEEE Transactions on Medical Imaging, Aug. 2009, 28:8:1251-1265.
[cited by applicant]
Hibar er al., “Genetic Clustering on the Hippocampal Surface for Genome-Wide Association Studies,” MICCAI., 2013, 16:2:690-7.
[cited by applicant]
Hu et al., “Automated Separation of Binary Overlapping Trees in Low-Contrast Color Retinal Images, ” MICCAI., 2013, 16:2:436-43.
[cited by applicant]
Huang et al., “A New Sparse Simplex Model for Brain Anatomical and Genetic Network Analysis, ” MICCAI., 2013, 16:2:625-632.
[cited by applicant]
Huh et al., “Apoptosis Detection for Non-adherent Cells in Time-lapse Phase Contrast Microscopy,” MICCAI, 2013, 8150: 59-66.
[cited by applicant]
Hussein et al., “Lung and Pancreatic Tumor Characterization in the Deep Learning Era: Novel Supervised and Unsupervised Learning Approaches”, IEEE Transactions on Medical Imaging, 2019, 38(8): 1777-1787.
[cited by applicant]
Hussein et al., “Supervised and unsupervised tumor characterization in the deep learning era,” ArXiv, 2018.
[cited by applicant]
Iglesias et al., Multi-atlas segmentation of biomedical images: A survey, Med Image Anal., Aug. 2015, 24:1:205-219.
[cited by applicant]
Imani et al., “Ultrasound-Based Characterization of Prostate Cancer: An in vivo Clinical Feasibility Study,” MICAAI, 2013, 16:2:279-86.
[cited by applicant]
International Preliminary Report on Patentability in Appl. No. PCT/US2020/029631, dated Sep. 28, 2021, 6 pages.
[cited by applicant]
International Preliminary Report on Patentability in International Application No. PCT/US2020/060061, mailed on May 27, 2022, 9 pages.
[cited by applicant]
International Search Report and Written Opinion in International Application No. PCT/US2020/060061, mailed on Mar. 1, 2021, 14 pages.
[cited by applicant]
International Search Report in Appl. No. PCT/US2020029631, dated Jul. 30, 2020, 6 pages.
[cited by applicant]
Jiang et al., “Predictive Models of Resting State Networks for Assessment of Altered Functional Connectivity in MCI,” MICCAI, 2013, 16:2:674-81.
[cited by applicant]
Jimenez del Toro et al., “Epileptogenic Lesion Quantification in MRI Using Contralateral 3D Texture Comparisons,” MICCAI., 2013, 16:2:353-60.
[cited by applicant]
Kamnitsas et al., “Effcient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation,” Med. Image Analysis, Feb. 2017, 36:61-78.
[cited by applicant]
Karasawa e al., “Multi-atlas pancreas segmentation: Atlas selection based on vessel structure,” Med. Image Analysis, 2017, 39:18-28.
[cited by applicant]
Kirbas et al., “A review of vessel extraction techniques and algorithms,” ACM Computing Surveys, 2004, 36:2:81-121.
[cited by applicant]
Koenig et al., “The Spatial Periodicity of Grid Cells Is Not Sustained During Reduced Theta Oscillations,” Science, 2011, 332:592-595.
[cited by applicant]
Kong et al., “RON: Reverse connection with objectness prior networks for object detection,” IEEE Conference on Computer Vision Pattern Recognition, Jul. 2017, 5936-5944.
[cited by applicant]
Kortylewski et al., “Compositional Convolutional Neural Networks: A Deep Architecture with Innate Robustness to Partial Occlusion,” arXiv:2003.04490v3, Apr. 2020, 10 pages.
[cited by applicant]
Krizkevsky et al., “ImageNet Classification with Deep Convolutional Neural Networks”, NIPS, 9 pages, 2012.
[cited by applicant]
Kroeger et al., “Learning to Segment Neurons with Non-local Quality Measures,” MICCAI., 2013, 16:2:419-27.
[cited by applicant]
Kronman et al., “Image Segmentation Errors Correction by Mesh Segmentation and Deformation,” MICCAI., 2013, 206-213.
[cited by applicant]
Kwitt et al., “Studying Cerebral Vasculature Using Structure Proximity and Graph Kernels,” MICCAI., 2013, 16:2:534-541.
[cited by applicant]
Lesage et al., “A review of 3D vessel lumen segmentation techniques: Models, features and extraction schemes,” SciDirect., 2009, 13:819-845.
[cited by applicant]
Li et al., “Automatic liver segmentation based shape constraints and deformable graph cut in CT images,” IEEE tans. On Image Proc., Dec. 2015, 24:12.
[cited by applicant]
Liao et al., “Representation Learning: A Unified Deep Learning Framework for Automatic Prostate MR Segmentation,” MICCAI., 2013, 16:2:254-61.
[cited by applicant]
Lindner et al., “Accurate Bone Segmentation in 2D Radiographs Using Fully Automatic Shape Model Matching Based On Regression-Voting,” MICCAI., 2013, 181-189.
[cited by applicant]
Liu et al., “Automatic pancreas segmentation via coarse location and ensemble learning,” IEEE Access, 2019, 9 pages.
[cited by applicant]
Liu et al., “High-Order Graph Matching Based Feature Selection for Alzheimer's Disease Identification,” MICCAI, 2013, 16:2:311-318.
[cited by applicant]
Liu et al., “A Variational Framework for Joint Detection and Segmentation of Ovarian Cancer Metastases,” MICCAI., 2013, 8150:83-90.
[cited by applicant]
Liu et al., “Joint shape representation and classification for detecting PDAC,” MLMI@MICCAI, 2019.
[cited by applicant]
Liu et al., “Longitudinal Modeling of Glaucoma Progression Using 2-Dimensional Continuous-Time Hidden Markov Model,” MICCAI., 2013, 16:2:444-451.
[cited by applicant]
Liu et al., “Multifold Bayesian Kernelization in Alzheimer's Diagnosis,” MICCAI., 2013, 16:2:303-10.
[cited by applicant]
Liu et al., “Tracking of Carotid Arteries in Ultrasound Images,” MICCAI., 2013, 16:2:526-33.
[cited by applicant]
Lombaert et al., “Joint Statistics on Cardiac Shape and Fiber Architecture,” MICCAI., 2013, 16:2:492-500.
[cited by applicant]
Long et al., “Fully Convolutional Networks for Semantic Segmentation,” CVPR, 2015, 3431-3440.
[cited by applicant]
Lorenzi et al., “Sparse Scale-Space Decomposition of Volume Changes in Deformations Fields,” MICCAI., 2013, 16:2:328-35.
[cited by applicant]
Lorza et al., “Carotid Artery Lumen Segmentation in 3D Free-Hand Ultrasound Images Using Surface Graph Cuts,” MICCAI., 2013, 16:2: 542-9.
[cited by applicant]
Lugo-fagundo et al., “Deep learning in radiology: Now the real work begins,” J. Am. Coll. Radiol., Feburary 2018, 15:2:364-367.
[cited by applicant]
Lundervold et al., “An overview of deep learning in medical imaging focusing on MRI,” Zeitschrift für Medizinische Physik, May 2019, 29:2:102-127.
[cited by applicant]
Lv et al., “Group-Wise FMRI Activation Detection on Corresponding Cortical Landmarks,” MICCAI., 2013, 16:2:665-73.
[cited by applicant]
Lv et al., “Modeling Dynamic Functional Information Flows on Large-Scale Brain Networks,” MICCAI., 2013, 698-705.
[cited by applicant]
Mahapatra et al., “Semi-Supervised and Active Learning for Automatic Segmentation of Crohn's Disease,” MICCAI., 2013, 16:2:214-21.
[cited by applicant]
Mahrooghy et al., “Heterogeneity Wavelet Kinetics from DCE-MRI for Classifying Gene Expression Based Breast Cancer Recurrence Risk,” MICCA., 2013, 16:2:295-302.
[cited by applicant]
Maier et al, “A gentle introduction to deep learning in medical imaging processing,” J. of Med. Physics, Dec. 2018.
[cited by applicant]
Marreiros et al., “Non-rigid Deformation Pipeline for Compensation of Superficial Brain Shift,” MICCAI., 2013, 16:2:141-8.
[cited by applicant]
McLeod et al., “Spatio-temporal Dimension Reduction of Cardiac Motion for Group-Wise Analysis and Statistical Testing,” MICCAI., 2013, 16:2:501-8.
[cited by applicant]
Melbourne et al., “Measurement of Myelin in the Preterm Brain: Multi-compartment Diffusion Imaging and Multi-component T2 Relaxometry,” MICCAI., 2013, 16:2:336-44.
[cited by applicant]
Mharib et al., “Survey on liver CT image segmentation methods,” Artif Intell rev., 2012, 37:83-95.
[cited by applicant]
Milletari et al., “V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation”, 2016 Fourth International Conference on 3D Vision,2016, 565-571.
[cited by applicant]
Mori et al., Medical Image computing and computer-assisted intervention-MICCAI 2013, 16th Int. Conf., Sep. 2013, 36 pages.
[cited by applicant]
Nascimento et al., Multi-atlas segmentation using manifold learning with deep belief neworks, IEE, 2016, 4 pages.
[cited by applicant]
NCBI.gov [online], “Pancreatic cancer treatment (adult) (PDQ),” May 15, 2020, retrieved on Oct. 5, 2020, retrieved from URL<https://www.ncbi.nlm.nih.gov/books/NBK65957/?report-printable. 37 pages.
[cited by applicant]
Nouranian et al., “An Automatic Multi-atlas Segmentation of the Prostate in Transrectal Ultrasound Images Using Pairwise Atlas Shape Similarity,” MICCAI, 2013, 16:2:173-80.
[cited by applicant]
Okada et al., “Abdominal multi-organ segmentation from CT images using conditional shape-location and unsupervised intensity priors,” Med. Image Anal., Dec. 2015, 26:1:1-18.
[cited by applicant]
Oktay et al., “Biomechanically driven registration of pre- to intra-operative 3D images for laparoscopic surgery,” MICCAI., 2013, 16:2:1-9.
[cited by applicant]
Ospina et al., “A Tensor-Based Population Value Decomposition to Explain Rectal Toxicity after Prostate Cancer Radiotherapy,” MICCAI., 2013, 16:2:387-94.
[cited by applicant]
Petersen et al., “Quantitative Airway Analysis in Longitudinal Studies Using Groupwise Registration and 4D Optimal Surfaces,” MICCAI., 2013, 16:2:287-94.
[cited by applicant]
Piuze et al., “Cardiac Fiber Inpainting Using Cartan Forms,” MICCAI, 2013, 16:2:509-17.
[cited by applicant]
Porras et al., “Myocardial Motion Estimation Combining Tissue Doppler and B-mode Echocardiographic Images, ” MICCAI., 2013, 16:2:484-91.
[cited by applicant]
Prasoon et al., “Deep Feature Learning for Knee Cartilage Segmentation Using a Triplanar Convolutional Neural Network,” MICCAI., 2013, 16:2:246-53.
[cited by applicant]
Prevost et al., “Registration of Free-Breathing 3D+t Abdominal Perfusion CT Images via Co-segmentation,” MICCAI., 2013, 16:2:99-107.
[cited by applicant]
Qiu et al., “Fast Globally Optimal Segmentation of 3D Prostate MRI with Axial Symmetry Prior,” MICCAI., 2013, 16:2:198-205.
[cited by applicant]
Rafii-Tari et al., “Learning-Based Modeling of Endovascular Navigation for Collaborative Robotic Catheterization,” MICCAI., 2013, 16:2:369-77.
[cited by applicant]
Rajchl et al., “Deepcut: Object segmentation from bounding box annotations using convulutional neural networks,” IEE Trans. Med. Imaging., 2016, 10 pages.
[cited by applicant]
Romain et al., “A Multi-task Learning Approach for Compartmental Model Parameter Estimation in DCE-CT Sequences,”MICAA., 2013, 16:2:271-8.
[cited by applicant]
Ronneberger et al., “U-Net: Convolutional Networks for Biomedical Image Segmentation”, MICCAR, 234-241, 2015.
[cited by applicant]
Roth et al., “Deep convolutional networks for pancreas segmentation in CT imaging,” SPIE Med., Imaging, 2015, 9413-94131G.
[cited by applicant]
Roth et al., “DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation”, MICCAI, 556-564, 2015.
[cited by applicant]
Roth et al., “Hierarchical 3D fully convolutional networks for multi-organ segmentation,” ARXIV., Apr. 2017, arXiv:1704.06382:11 pages.
[cited by applicant]
Roth et al., Spatial aggregation of holistically-nested convolutional neural networks for automated pancreas localization and segmentation, Medical Image Analysis, Apr. 2018, 45:94-107.
[cited by applicant]
Roth et al., “Spatial Aggregation of Holistically-Nested Networks for Automated Pancreas Segmentation”, 2016, MICCAI, 451-459.
[cited by applicant]
Roth et al., “Towards dense volumetric pancreas segmentation in CT using 3D fully conventional networks,” Medical Imaging, 2018, 6 pages.
[cited by applicant]
Sabuncu et al., “A generative model for image segmentation based on label fusion,” IEE Trans. On Med. Imaging, Oct. 2010, 29:10:1714-1729.
[cited by applicant]
Saito et al., “Joint optimization of segmentation and shape prior from level-set-based statistical shape model, and its application to the automated segmentation of abdominal organs,” Med. Image Analysis, 2016, 28:46-65.
[cited by applicant]
Seito et al., “Pulmonary nodule detection in CT images: False positive reduction using multi-view convolutional networks,” IEE Trans. On Med. Imag., May 2016, 35:5:1160-1169.
[cited by applicant]
Shen et al., “A Spatial Mixture Approach to Inferring Sub-ROI Spatio-temporal Patterns from Rapid Event-Related fMRI Data,” MICCAI., 2013, 16:2:657-64.
[cited by applicant]
Shen et al., Deep learning in medical image analysis, Annu rev.Biomed. En., 2017, 19:221-248.
[cited by applicant]
Simpson et al., “A bayesian approach for spatially adaptive regularisation in Non-rigid registration,” MICCAI., 2013, 10-18.
[cited by applicant]
Song et al., “Discriminative Data Transform for Image Feature Extraction and Classification,” MICCAI., 2013, 16:2:452-9.
[cited by applicant]
Suk et al., “Deep Learning-Based Feature Representation for AD/MCI Classification,” MICCAI., 2013, 16:2:583-90.
[cited by applicant]
Tang et al., “Random Walks with Efficient Search and Contextually Adapted Image Similarity for Deformable Registration,” MICCAI, 2013, 43-50.
[cited by applicant]
Tong et al., “Multiple Instance Learning for Classification of Dementia in Brain MRI,” Med. Image Anal., Jul. 2014, 18:5:808-18.
[cited by applicant]
Uzunbas et al., “Collaborative Multi Organ Segmentation by Integrating Deformable and Graphical Models,” MICCAI, 2013, 157-164.
[cited by applicant]
Vincent et al., “Bayesian Joint Detection-Estimation of Cerebral Vasoreactivity from ASL fMRI Data,” MICCAI, 2013, 616-624.
[cited by applicant]
Wang et al., “Image quality assessment: Form error visibility to structural similarity,” IEE. Trans. On Image. Proces., Apr. 2004, 13:4:600-612.
[cited by applicant]
Wang et al., “Interactive Retinal Vessel Extraction by Integrating Vessel Tracing and Graph Search,” MICCAI., 2013, 16:2:567-74.
[cited by applicant]
Wang et al., “Automated Embryo Stage Classification in Time-Lapse Microscopy Video of Early Human Embryo Development,” MICCAI., 2013, 16:2:460-7.
[cited by applicant]
Wang et al., “Image Quality Assessment: From Error Visibility to Structural Similarity,” IEEE Transactions on Image Processing, Apr. 2004, 13:4:600-612.
[cited by applicant]
Wang et al., “Robust Object Detection under Occlusion with Context-Aware CompositionalNets,” 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2020, pp. 12642-12651.
[cited by applicant]
Wang et al., “Sequential Monte Carlo Tracking for Marginal Artery Segmentation on CT Angiography by Multiple Cue Fusion,” MICCAI., 2013, 16:2:518-525.
[cited by applicant]
Wang et al., “Abdominal multi-organ segmentation with organ-attention networks and statistical fusion,” Medical Image Analysis, 2019, 55:88-102.
[cited by applicant]
Warfield et al., “Simultaneous truth and performance level estimation (STAPLE): An algorithm for the validation of image segmentation,” IEE Trans. Med. Imaging., Jul. 2004, 23:7:903-921.
[cited by applicant]
Wasza et al., “Real-Time Respiratory Motion Analysis Using Manifold Ray Casting of Volumetrically Fused Multi-view Range Imaging,” MICCAI., 2013, 16:2:116-23.
[cited by applicant]
Wee et al., “Identification of MCI Using Optimal Sparse MAR Modeled Effective Connectivity Networks,” MICCAI., 2013, 16:2:319-327.
[cited by applicant]
Wolz et al., “Automated abdominal multi-organ segmentation with subject-specific atlas generation,” IEE Trans. On Med. Imaging, Sep. 2013, 32:9:1723-1730.
[cited by applicant]
Wu et al., “Unsupervised Deep Feature Learning for Deformable Registration of MR Brain Images,” MICCAI., 2013, 16:2:649-56.
[cited by applicant]
Xia et al., “Bridging the Gap Between 2D and 3D Organ Segmentation with Volumetric Fusion Net”, MICCAI, 445-453, 2018.
[cited by applicant]
Xie et al., “Holistically-Nested Edge Detection”, Proceedings of the IEEE International Conference on Computer Vision, 2015, 1395-1403.
[cited by applicant]
Xu et al., “A Symmetric 4D Registration Algorithm for Respiratory Motion Modeling,” MICCAI., 2013, 16:2:149-56.
[cited by applicant]
Xu et al., “Automatic Grading of Nuclear Cataracts from Slit-Lamp Lens Images Using Group Sparsity Regression,” MICCAI., 2013, 16:2:468-75.
[cited by applicant]
Xu et al., “Spatially Constrained Random Walk Approach for Accurate Estimation of Airway Wall Surfaces,” MICCAI., 2013, 16:2:559-566.
[cited by applicant]
Ye et al., “Pathological Site Retargeting under Tissue Deformation Using Geometrical Association and Tracking, ” MICCAI., 2013, 16:2:67-74.
[cited by applicant]
Yokota et al., “Automated CT Segmentation of Diseased Hip Using Hierarchical and Conditional Statistical Shape Models,” MICCAI, 2013 190-197.
[cited by applicant]
Yoldemir et al., “Overlapping Replicator Dynamics for Functional Subnetwork Identification,” MICCAI., 2013, 16:2:682-9.
[cited by applicant]
Yu et al., “Recurrent saliency transformation network: incorporating multi-stage visual cues for small organ segmentation,” Arxiv, 2017.
[cited by applicant]
Yuille et al., “Abdominal multi-organ segmentation with organ-attention networks and statistical fusion,” Med., Image Anal., Jul. 2019, 55:88-102.
[cited by applicant]
Zhang et al., “Large Deformation Diffeomorphic Registration of Diffusion-Weighted Images with Explicit Orientation Optimization”.
[cited by applicant]
Zhang et al., “Personalized Pancreatic Tumor Growth Prediction via Group Learning”, MICCAI, 2017, 424-432.
[cited by applicant]
Zhao et al., “Hierarchical Constrained Local Model Using ICA and Its Application to Down Syndrome Detection,” MICCAI., 2013, 16:2:222-9.
[cited by applicant]
Zheng et al., “Optic disc and cup segmentation from color fundus photograph using graph cut with priors,” MICCAI., 2013, 16:75-82.
[cited by applicant]
Zhou et al., “A Fixed-Point Model for Pancreas Segmentation in Abdominal CT Scans”, MICCAI,2017, 693-701.
[cited by applicant]
Zhou et al., “Deep Supervision for Pancreatic Cyst Segmentation in Abdominal CT Scans”, MICCAI, 2017, 222-230,.
[cited by applicant]
Zhou et al., “Pancreas Segmentation in Abdominal CT Scan: A Coarse-to-Fine Approach.” arXiv:1612.08230v1, Dec. 2016, pp. 1-13.
[cited by applicant]
Zhu et al., “A 3D Coarse-to-Fine Framework for Volumetric Medical Image Segmentation”, International Conference on 3D Vision, 682-690, 2018.
[cited by applicant]
Zhu et al., “Deep Learning Representation using Autoencoder for 3D Shape Retrieval”, Neurocomputing 204: 41-50, 2016.
[cited by applicant]
Zhu et al., “Multi-scale coarse-to-fine segmentation for screening pancreatic ductal adenocarcinoma,” ArXiv, 2019.
[cited by applicant]
Zhu et al., “Random Walks with Adaptive Cylinder Flux Based Connectivity for Vessel Segmentation,” MICCAI., 2013, 16:2:550-8.
[cited by applicant]
Zhuang et al., Multi-scale patch and multi-modality atlases for whole heart segmentation of MRI, Med Image Analysis, 2016, 31:77-87.
[cited by applicant]
Zu et al., “Robust multi-atlas propagation by deep sparse representation,” Pattern Recognition, 2017, 63:511-517.
[cited by applicant]
Cen et al., “Boosting occluded image classification via subspace decomposition-based estimation of deep features,” IEEE Transactions on Cybernetics, Aug. 12, 2019, 50(7):3409-22.
[cited by applicant]
Cen et al., “Deep feature augmentation for occluded image classification,” Pattern Recognition, Mar. 1, 2021, 111:107737.
[cited by applicant]
Enzweiler et al., “Multi-cue pedestrian classification with partial occlusion handling,” In 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Jun. 13, 2010, 990-997.
[cited by applicant]
Kortylewski et al., “Combining compositional models and deep networks for robust object classification under occlusion,” In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2020, 1333-13…
[cited by applicant]
Yoo, “Deep convolution neural networks in computer vision: a review,” IEIE Transactions on Smart Processing and Computing, Feb. 2015, 4(1):35-43.
[cited by applicant]