Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis: Second International Workshop, UNSURE 2020, and Third International Workshop, GRAIL 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, Proceedings

We develop a fully Bayesian framework for non-rigid registration of three-dimensional medical images, with a focus on uncertainty quantification. Probabilistic registration of large images along with calibrated uncertainty estimates is difficult for both computational and modelling reasons. To address the computational issues, we explore connections between the Markov chain Monte Carlo by backprop and the variational inference by backprop frameworks in order to efficiently draw thousands of samples from the posterior distribution. Regarding the modelling issues, we carefully design a Bayesian model for registration to overcome the existing barriers when using a dense, high-dimensional, and diffeomorphic parameterisation of the transformation. This results in improved calibration of uncertainty estimates.

[1]  Ender Konukoglu,et al.  PHiSeg: Capturing Uncertainty in Medical Image Segmentation , 2019, MICCAI.

[2]  Christian Payer,et al.  Integrating spatial configuration into heatmap regression based CNNs for landmark localization , 2019, Medical Image Anal..

[3]  Peer-Timo Bremer,et al.  Building Calibrated Deep Models via Uncertainty Matching with Auxiliary Interval Predictors , 2020, AAAI.

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

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

[6]  Kilian Q. Weinberger,et al.  On Calibration of Modern Neural Networks , 2017, ICML.

[7]  Alan Connelly,et al.  Anatomically-constrained tractography: Improved diffusion MRI streamlines tractography through effective use of anatomical information , 2012, NeuroImage.

[8]  Vince D. Calhoun,et al.  Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls , 2017, NeuroImage.

[9]  Georg Langs,et al.  Exploiting Epistemic Uncertainty of Anatomy Segmentation for Anomaly Detection in Retinal OCT , 2019, IEEE Transactions on Medical Imaging.

[10]  Yann LeCun,et al.  Transforming Neural-Net Output Levels to Probability Distributions , 1990, NIPS.

[11]  Jose Dolz,et al.  Constrained domain adaptation for segmentation , 2019, MICCAI.

[12]  Daniel Rueckert,et al.  The Effect of Preterm Birth on Thalamic and Cortical Development , 2011, Cerebral cortex.

[13]  Daniel Cohen-Or,et al.  MeshCNN: a network with an edge , 2019, ACM Trans. Graph..

[14]  A.D. Hoover,et al.  Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response , 2000, IEEE Transactions on Medical Imaging.

[15]  Martin Vetterli,et al.  Adaptive wavelet thresholding for image denoising and compression , 2000, IEEE Trans. Image Process..

[16]  Pascal Fua,et al.  SLIC Superpixels Compared to State-of-the-Art Superpixel Methods , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  M. Bastin,et al.  Neonatal morphometric similarity mapping for predicting brain age and characterizing neuroanatomic variation associated with preterm birth , 2019, NeuroImage: Clinical.

[18]  Victor S. Lempitsky,et al.  Unsupervised Domain Adaptation by Backpropagation , 2014, ICML.

[19]  Kaspar Riesen,et al.  Approximate graph edit distance computation by means of bipartite graph matching , 2009, Image Vis. Comput..

[20]  Linda G. Shapiro,et al.  From patch-level to ROI-level deep feature representations for breast histopathology classification , 2019, Medical Imaging.

[21]  Pierre Besson,et al.  Geometric deep learning on brain shape predicts sex and age , 2020, bioRxiv.

[22]  J. Allsop,et al.  Thalamocortical Connectivity Predicts Cognition in Children Born Preterm , 2015, Cerebral cortex.

[23]  Nico Karssemeijer,et al.  Using deep convolutional neural networks to identify and classify tumor-associated stroma in diagnostic breast biopsies , 2018, Modern Pathology.

[24]  Christian Wachinger,et al.  Deep Multi-Structural Shape Analysis: Application to Neuroanatomy , 2018, MICCAI.

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

[26]  Zhuowen Tu,et al.  Similarity network fusion for aggregating data types on a genomic scale , 2014, Nature Methods.

[27]  Horst Bischof,et al.  Automatic Point Landmark Matching for Regularizing Nonlinear Intensity Registration: Application to Thoracic CT Images , 2006, MICCAI.

[28]  Graham W. Taylor,et al.  Leveraging Uncertainty Estimates for Predicting Segmentation Quality , 2018, ArXiv.

[29]  George R. Thoma,et al.  Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood smear images , 2018, PeerJ.

[30]  L. Bai,et al.  Loss of Microstructural Integrity in the Limbic-Subcortical Networks for Acute Symptomatic Traumatic Brain Injury , 2014, BioMed research international.

[31]  Tim Verbelen,et al.  Quantifying Uncertainty of Deep Neural Networks in Skin Lesion Classification , 2019, UNSURE/CLIP@MICCAI.

[32]  Jian Sun,et al.  Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[33]  Daniel Rueckert,et al.  The developing human connectome project: A minimal processing pipeline for neonatal cortical surface reconstruction , 2017, NeuroImage.

[34]  Yue Zhang,et al.  Task Driven Generative Modeling for Unsupervised Domain Adaptation: Application to X-ray Image Segmentation , 2018, MICCAI.

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

[36]  Aryan Mobiny,et al.  Risk-Aware Machine Learning Classifier for Skin Lesion Diagnosis , 2019, Journal of clinical medicine.

[37]  Yoshua Bengio,et al.  The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[38]  Paul J. Laurienti,et al.  A New Measure of Centrality for Brain Networks , 2010, PloS one.

[39]  Drew Parker,et al.  Connectomic consistency: a systematic stability analysis of structural and functional connectivity , 2019, bioRxiv.

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

[41]  Noel C. F. Codella,et al.  Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC) , 2019, ArXiv.

[42]  Andrew Melbourne,et al.  A Network-Based Analysis of the Preterm Adolescent Brain Using PCA and Graph Theory , 2020, Computational Diffusion MRI.

[43]  Jie Li,et al.  An Attention-Guided Deep Regression Model for Landmark Detection in Cephalograms , 2019, MICCAI.

[44]  Pierre Vandergheynst,et al.  Geometric Deep Learning: Going beyond Euclidean data , 2016, IEEE Signal Process. Mag..

[45]  Xuelong Li,et al.  A survey of graph edit distance , 2010, Pattern Analysis and Applications.

[46]  Bo Wang,et al.  Unsupervised metric fusion by cross diffusion , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[47]  Max Welling,et al.  Auto-Encoding Variational Bayes , 2013, ICLR.

[48]  Zoubin Ghahramani,et al.  Probabilistic machine learning and artificial intelligence , 2015, Nature.

[49]  Nima Tajbakhsh,et al.  Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentation , 2019, Medical Image Anal..

[50]  Klaus H. Maier-Hein,et al.  A Probabilistic U-Net for Segmentation of Ambiguous Images , 2018, NeurIPS.

[51]  Pietro Liò,et al.  Graph Attention Networks , 2017, ICLR.

[52]  H. Sebastian Seung,et al.  Natural Image Denoising with Convolutional Networks , 2008, NIPS.

[53]  Aifeng Zhang,et al.  Racial differences in growth patterns of children assessed on the basis of bone age. , 2009, Radiology.

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

[55]  Xiaohui Xie,et al.  Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification , 2016, bioRxiv.

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

[57]  O. Sporns,et al.  A cross-disorder connectome landscape of brain dysconnectivity , 2019, Nature Reviews Neuroscience.

[58]  Heikki Huttunen,et al.  Machine learning framework for early MRI-based Alzheimer's conversion prediction in MCI subjects , 2015, NeuroImage.

[59]  Samuel S. Schoenholz,et al.  Neural Message Passing for Quantum Chemistry , 2017, ICML.

[60]  Honglak Lee,et al.  Adaptive Multi-Column Deep Neural Networks with Application to Robust Image Denoising , 2013, NIPS.

[61]  Neil Marlow,et al.  Neuropsychological Outcomes at 19 Years of Age Following Extremely Preterm Birth , 2020, Pediatrics.

[62]  Jin Tae Kwak,et al.  Hover-Net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images , 2018, Medical Image Anal..

[63]  Cigdem Demir,et al.  The cell graphs of cancer , 2004, ISMB/ECCB.

[64]  Mario Vento,et al.  Thirty Years Of Graph Matching In Pattern Recognition , 2004, Int. J. Pattern Recognit. Artif. Intell..

[65]  Michael I Miga,et al.  Characterization and correction of intraoperative soft tissue deformation in image-guided laparoscopic liver surgery , 2017, Journal of medical imaging.

[66]  Natalia Gimelshein,et al.  PyTorch: An Imperative Style, High-Performance Deep Learning Library , 2019, NeurIPS.

[67]  Adrien Bartoli,et al.  Preoperative liver registration for augmented monocular laparoscopy using backward–forward biomechanical simulation , 2018, International Journal of Computer Assisted Radiology and Surgery.

[68]  Nicholas Ayache,et al.  A Log-Euclidean Framework for Statistics on Diffeomorphisms , 2006, MICCAI.

[69]  Christian Desrosiers,et al.  Graph Convolutions on Spectral Embeddings for Cortical Surface Parcellation , 2019, Medical Image Anal..

[70]  Surabhi Bhargava,et al.  A Dataset and a Technique for Generalized Nuclear Segmentation for Computational Pathology , 2017, IEEE Transactions on Medical Imaging.

[71]  I. Timor-Tritsch,et al.  Sonographic examination of the fetal central nervous system: guidelines for performing the ‘basic examination’ and the ‘fetal neurosonogram’ , 2007, Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology.

[72]  Max Welling,et al.  Semi-supervised Learning with Deep Generative Models , 2014, NIPS.

[73]  Wenping Wang,et al.  Cephalometric Landmark Detection by AttentiveFeature Pyramid Fusion and Regression-Voting , 2019, MICCAI.

[74]  Ching-Wei Wang,et al.  Fully Automatic System for Accurate Localisation and Analysis of Cephalometric Landmarks in Lateral Cephalograms , 2016, Scientific Reports.

[75]  Edmond Boyer,et al.  FeaStNet: Feature-Steered Graph Convolutions for 3D Shape Analysis , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[76]  Ming-Hsuan Yang,et al.  Learning to Adapt Structured Output Space for Semantic Segmentation , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[77]  Adrien Bartoli,et al.  Deformable Registration of a Preoperative 3D Liver Volume to a Laparoscopy Image Using Contour and Shading Cues , 2017, MICCAI.

[78]  Andrew Gordon Wilson,et al.  A Simple Baseline for Bayesian Uncertainty in Deep Learning , 2019, NeurIPS.

[79]  Matthew B. Blaschko,et al.  Artery-vein segmentation in fundus images using a fully convolutional network , 2019, Comput. Medical Imaging Graph..

[80]  Islem Rekik,et al.  Estimation of connectional brain templates using selective multi-view network normalization , 2020, Medical Image Anal..

[81]  Charles Blundell,et al.  Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles , 2016, NIPS.

[82]  Islem Rekik,et al.  Clustering-based multi-view network fusion for estimating brain network atlases of healthy and disordered populations , 2019, Journal of Neuroscience Methods.

[83]  Rosanna Grassi,et al.  Some New Results on the Eigenvector Centrality , 2007 .

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

[85]  Zhi-Li Zhang,et al.  Hunt For The Unique, Stable, Sparse And Fast Feature Learning On Graphs , 2017, NIPS.

[86]  Anant Madabhushi,et al.  Semi Supervised Multi Kernel (SeSMiK) Graph Embedding: Identifying Aggressive Prostate Cancer via Magnetic Resonance Imaging and Spectroscopy , 2010, MICCAI.

[87]  M. Raichle,et al.  Disease and the brain's dark energy , 2010, Nature Reviews Neurology.

[88]  Xiaohu Guo,et al.  Spectral mesh deformation , 2008, The Visual Computer.

[89]  Jianxiong Xiao,et al.  3D ShapeNets: A deep representation for volumetric shapes , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[90]  Suzanne T. Witt,et al.  Multiple resting state network functional connectivity abnormalities in mild traumatic brain injury , 2012, Brain Imaging and Behavior.

[91]  Jure Leskovec,et al.  Inductive Representation Learning on Large Graphs , 2017, NIPS.

[92]  Konstantinos Kamnitsas,et al.  A deep learning approach to segmentation of the developing cortex in fetal brain MRI with minimal manual labeling , 2020, MIDL.

[93]  Torsten Rohlfing,et al.  The INIA19 Template and NeuroMaps Atlas for Primate Brain Image Parcellation and Spatial Normalization , 2012, Front. Neuroinform..

[94]  Henkjan Huisman,et al.  Supervised Uncertainty Quantification for Segmentation with Multiple Annotations , 2019, MICCAI.

[95]  Harri Valpola,et al.  Weight-averaged consistency targets improve semi-supervised deep learning results , 2017, ArXiv.

[96]  Jasjit S Suri,et al.  State-of-the-art review on deep learning in medical imaging. , 2019, Frontiers in bioscience.

[97]  S. Swinnen,et al.  Brain connectivity and postural control in young traumatic brain injury patients: A diffusion MRI based network analysis☆☆☆ , 2012, NeuroImage: Clinical.

[98]  Bernhard Kainz,et al.  Exploring the Relationship Between Segmentation Uncertainty, Segmentation Performance and Inter-observer Variability with Probabilistic Networks , 2019, LABELS/HAL-MICCAI/CuRIOUS@MICCAI.

[99]  Martin Urschler,et al.  Integrating geometric configuration and appearance information into a unified framework for anatomical landmark localization , 2018, Medical Image Anal..

[100]  Dominique Makowski,et al.  bayestestR: Describing Effects and their Uncertainty, Existence and Significance within the Bayesian Framework , 2019, J. Open Source Softw..

[101]  Alicja R. Rudnicka,et al.  Automated arteriole and venule classification using deep learning for retinal images from the UK Biobank cohort , 2017, Comput. Biol. Medicine.

[102]  Purang Abolmaesumi,et al.  Variational Shape Completion for Virtual Planning of Jaw Reconstructive Surgery , 2019, MICCAI.

[103]  Bostjan Likar,et al.  Shape Representation for Efficient Landmark-Based Segmentation in 3-D , 2014, IEEE Transactions on Medical Imaging.

[104]  Sébastien Ourselin,et al.  Automatic Brain Tumor Segmentation Based on Cascaded Convolutional Neural Networks With Uncertainty Estimation , 2019, Front. Comput. Neurosci..

[105]  David J. C. MacKay,et al.  A Practical Bayesian Framework for Backpropagation Networks , 1992, Neural Computation.

[106]  I. Rekik,et al.  Gender differences in cortical morphological networks , 2019, Brain Imaging and Behavior.

[107]  M. Greicius,et al.  Default-mode network activity distinguishes Alzheimer's disease from healthy aging: Evidence from functional MRI , 2004, Proc. Natl. Acad. Sci. USA.

[108]  Junjie Zhu,et al.  SIMLR: A Tool for Large‐Scale Genomic Analyses by Multi‐Kernel Learning , 2018, Proteomics.

[109]  Paul M. Thompson,et al.  Multi-source feature learning for joint analysis of incomplete multiple heterogeneous neuroimaging data , 2012, NeuroImage.

[110]  Rafeef Garbi,et al.  Comparative Evaluation of Hand-Engineered and Deep-Learned Features for Neonatal Hip Bone Segmentation in Ultrasound , 2019, MICCAI.

[111]  F. Pernus,et al.  A review of methods for quantitative evaluation of spinal curvature , 2009, European Spine Journal.

[112]  Bruce Fischl,et al.  Within-subject template estimation for unbiased longitudinal image analysis , 2012, NeuroImage.

[113]  H. K. Huang,et al.  Bone age assessment of children using a digital hand atlas , 2007, Comput. Medical Imaging Graph..

[114]  David J. Hawkes,et al.  Global rigid registration of CT to video in laparoscopic liver surgery , 2018, International Journal of Computer Assisted Radiology and Surgery.

[115]  Bernhard Schölkopf,et al.  Learning with Local and Global Consistency , 2003, NIPS.

[116]  Percy Liang,et al.  Calibrated Structured Prediction , 2015, NIPS.

[117]  Timothy E. Ham,et al.  Default Mode Network Connectivity Predicts Sustained Attention Deficits after Traumatic Brain Injury , 2011, The Journal of Neuroscience.

[118]  Lüder A. Kahrs,et al.  Well-Calibrated Regression Uncertainty in Medical Imaging with Deep Learning , 2020, MIDL.

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

[120]  Olaf Sporns,et al.  The Human Connectome: A Structural Description of the Human Brain , 2005, PLoS Comput. Biol..

[121]  Lasse Lensu,et al.  On the Uncertainty of Retinal Artery-Vein Classification with Dense Fully-Convolutional Neural Networks , 2020, ACIVS.

[122]  Andrea Vedaldi,et al.  Deep Image Prior , 2017, International Journal of Computer Vision.

[123]  Tommy W. S. Chow,et al.  Compact Graph based Semi-Supervised Learning for Medical Diagnosis in Alzheimer’s Disease , 2014, IEEE Signal Processing Letters.

[124]  J. Elmore,et al.  Diagnostic concordance among pathologists interpreting breast biopsy specimens. , 2015, JAMA.

[125]  Bruce R. Rosen,et al.  Brain Genomics Superstruct Project initial data release with structural, functional, and behavioral measures , 2015, Scientific Data.

[126]  Tolga Tasdizen,et al.  Domain adaptation for biomedical image segmentation using adversarial training , 2018, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).

[127]  Hossein Rabbani,et al.  A Comprehensive Study of Retinal Vessel Classification Methods in Fundus Images , 2017, Journal of medical signals and sensors.

[128]  Islem Rekik,et al.  Machine learning methods for brain network classification: Application to autism diagnosis using cortical morphological networks , 2020, Journal of Neuroscience Methods.

[129]  Yiqiang Zhan,et al.  Dynamic Spectral Graph Convolution Networks with Assistant Task Training for Early MCI Diagnosis , 2019, MICCAI.

[130]  Daniel Rueckert,et al.  A deformable model for the reconstruction of the neonatal cortex , 2017, 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017).

[131]  Daguang Xu,et al.  Automatic Vertebra Labeling in Large-Scale 3D CT using Deep Image-to-Image Network with Message Passing and Sparsity Regularization , 2017, IPMI.

[132]  Fabrice Wendling,et al.  Brain network similarity: methods and applications , 2019, Network Neuroscience.

[133]  Jure Leskovec,et al.  node2vec: Scalable Feature Learning for Networks , 2016, KDD.

[134]  Igor Kononenko,et al.  Machine learning for medical diagnosis: history, state of the art and perspective , 2001, Artif. Intell. Medicine.

[135]  V. Calhoun,et al.  Functional network connectivity during rest and task conditions: A comparative study , 2013, Human brain mapping.

[136]  Jan Sijbers,et al.  Denoising of diffusion MRI using random matrix theory , 2016, NeuroImage.

[137]  Shin Ishii,et al.  Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[138]  Timothy Woodacre,et al.  The costs of late detection of developmental dysplasia of the hip , 2013, Journal of children's orthopaedics.

[139]  David K. Hammond,et al.  Graph diffusion distance: A difference measure for weighted graphs based on the graph Laplacian exponential kernel , 2013, 2013 IEEE Global Conference on Signal and Information Processing.

[140]  Radford M. Neal.  Bayesian Learning via Stochastic Dynamics , 1992, NIPS.

[141]  Richard C. Pais,et al.  The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans. , 2011, Medical physics.

[142]  Christos Davatzikos,et al.  Differential cortical microstructural maturation in the preterm human brain with diffusion kurtosis and tensor imaging , 2019, Proceedings of the National Academy of Sciences.

[143]  G. Reinsel,et al.  Introduction to Mathematical Statistics (4th ed.). , 1980 .

[144]  Daniel Rueckert,et al.  Geodesic Information Flows: Spatially-Variant Graphs and Their Application to Segmentation and Fusion , 2015, IEEE Transactions on Medical Imaging.

[145]  Peter Bankhead,et al.  QuPath: Open source software for digital pathology image analysis , 2017, Scientific Reports.

[146]  Islem Rekik,et al.  Brain multiplexes reveal morphological connectional biomarkers fingerprinting late brain dementia states , 2018, Scientific Reports.

[147]  Giovanni Montana,et al.  Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker , 2016, NeuroImage.

[148]  M. Kendall.  Statistical Methods for Research Workers , 1937, Nature.

[149]  Pheng-Ann Heng,et al.  CGC-Net: Cell Graph Convolutional Network for Grading of Colorectal Cancer Histology Images , 2019, 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW).

[150]  Dinggang Shen,et al.  Estimation of Brain Network Atlases Using Diffusive-Shrinking Graphs: Application to Developing Brains , 2017, IPMI.

[151]  B. Ginneken,et al.  Automated measurement of fetal head circumference using 2D ultrasound images , 2018, PloS one.

[152]  Olaf Sporns,et al.  Comparative Connectomics , 2016, Trends in Cognitive Sciences.

[153]  Stamatios N. Sotiropoulos,et al.  An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging , 2016, NeuroImage.

[154]  Siegfried Wahl,et al.  Leveraging uncertainty information from deep neural networks for disease detection , 2016, Scientific Reports.

[155]  Patrick Pérez,et al.  ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[156]  Yoshua Bengio,et al.  Semi-supervised Learning by Entropy Minimization , 2004, CAP.

[157]  Paul A. Bromiley,et al.  Robust and Accurate Shape Model Matching Using Random Forest Regression-Voting , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[158]  Mauricio Reyes,et al.  On the Effect of Inter-observer Variability for a Reliable Estimation of Uncertainty of Medical Image Segmentation , 2018, MICCAI.

[159]  Islem Rekik,et al.  Unsupervised Manifold Learning Using High-Order Morphological Brain Networks Derived From T1-w MRI for Autism Diagnosis , 2018, Front. Neuroinform..

[160]  Alexei A. Efros,et al.  Image-to-Image Translation with Conditional Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[161]  Daniel C. Krawczyk,et al.  Disrupted Intrinsic Connectivity among Default, Dorsal Attention, and Frontoparietal Control Networks in Individuals with Chronic Traumatic Brain Injury* , 2016, Journal of the International Neuropsychological Society.

[162]  S. Weber,et al.  Efficiency, Accuracy and Clinical Applicability of a New Image-Guided Surgery System in 3D Laparoscopic Liver Surgery , 2019, Journal of Gastrointestinal Surgery.

[163]  Matthew D. Zeiler.  ADADELTA: An Adaptive Learning Rate Method , 2012, ArXiv.

[164]  Moon Gi Kang,et al.  Poisson–Gaussian Noise Analysis and Estimation for Low-Dose X-ray Images in the NSCT Domain , 2018, Sensors.

[165]  Bolei Zhou,et al.  Learning Deep Features for Discriminative Localization , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[166]  Daniel L Rubin,et al.  A curated mammography data set for use in computer-aided detection and diagnosis research , 2017, Scientific Data.

[167]  Steven Skiena,et al.  DeepWalk: online learning of social representations , 2014, KDD.

[168]  Satrajit S. Ghosh,et al.  Mindboggling morphometry of human brains , 2016, bioRxiv.

[169]  Ragini Verma,et al.  System-level matching of structural and functional connectomes in the human brain , 2019, NeuroImage.

[170]  Raymond Y. K. Lau,et al.  Least Squares Generative Adversarial Networks , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).

[171]  Gary E. Christensen,et al.  Consistent landmark and intensity-based image registration , 2002, IEEE Transactions on Medical Imaging.

[172]  M N Rossor,et al.  Patterns of temporal lobe atrophy in semantic dementia and Alzheimer's disease , 2001, Annals of neurology.

[173]  Yarin Gal,et al.  Uncertainty in Deep Learning , 2016 .

[174]  Anna Goldenberg,et al.  What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use , 2019, MLHC.

[175]  Wei Cai,et al.  Augmented reality navigation for liver resection with a stereoscopic laparoscope , 2020, Comput. Methods Programs Biomed..

[176]  Ke Li,et al.  Predicting Brain Age of Healthy Adults Based on Structural MRI Parcellation Using Convolutional Neural Networks , 2020, Frontiers in Neurology.

[177]  Hans-Peter Meinzer,et al.  Statistical shape models for 3D medical image segmentation: A review , 2009, Medical Image Anal..

[178]  Yang Zou,et al.  Domain Adaptation for Semantic Segmentation via Class-Balanced Self-Training , 2018, ArXiv.

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

[180]  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).

[181]  Alexander Zien,et al.  Semi-Supervised Classification by Low Density Separation , 2005, AISTATS.

[182]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[183]  Leonidas J. Guibas,et al.  PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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

[185]  Dinggang Shen,et al.  A Hybrid Multishape Learning Framework for Longitudinal Prediction of Cortical Surfaces and Fiber Tracts Using Neonatal Data , 2016, MICCAI.

[186]  Olaf Sporns,et al.  Weight-conserving characterization of complex functional brain networks , 2011, NeuroImage.

[187]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[188]  Jure Leskovec,et al.  Hierarchical Graph Representation Learning with Differentiable Pooling , 2018, NeurIPS.

[189]  Richard McElreath,et al.  Statistical Rethinking: A Bayesian Course with Examples in R and Stan , 2015 .

[190]  Leonidas J. Guibas,et al.  PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space , 2017, NIPS.

[191]  Hongyi Zhang,et al.  mixup: Beyond Empirical Risk Minimization , 2017, ICLR.

[192]  Stephane Cotin,et al.  Physics-Based Deep Neural Network for Augmented Reality During Liver Surgery , 2019, MICCAI.

[193]  W. Engle.  Age Terminology During the Perinatal Period , 2004, Pediatrics.

[194]  Li Shen,et al.  Deep Learning to Improve Breast Cancer Detection on Screening Mammography , 2017, Scientific Reports.

[195]  Nasullah Khalid Alham,et al.  Improving Whole Slide Segmentation Through Visual Context - A Systematic Study , 2018, MICCAI.

[196]  Claudio Perez Tamargo.  Can one hear the shape of a drum , 2008 .

[197]  Hayit Greenspan,et al.  Chest pathology detection using deep learning with non-medical training , 2015, 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI).

[198]  Jiaolong Yang,et al.  Go-ICP: A Globally Optimal Solution to 3D ICP Point-Set Registration , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[199]  Alessandro Foi,et al.  Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering , 2007, IEEE Transactions on Image Processing.

[200]  Yannis Avrithis,et al.  Label Propagation for Deep Semi-Supervised Learning , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[201]  Alan Connelly,et al.  SIFT2: Enabling dense quantitative assessment of brain white matter connectivity using streamlines tractography , 2015, NeuroImage.

[202]  Yoshua Bengio,et al.  Interpolation Consistency Training for Semi-Supervised Learning , 2019, IJCAI.

[203]  Ole Winther,et al.  Autoencoding beyond pixels using a learned similarity metric , 2015, ICML.

[204]  Brian B. Avants,et al.  N4ITK: Improved N3 Bias Correction , 2010, IEEE Transactions on Medical Imaging.

[205]  William M. Wells,et al.  A Feature-Based Developmental Model of the Infant Brain in Structural MRI , 2012, MICCAI.

[206]  K. Deisseroth,et al.  Repeated Cortico-Striatal Stimulation Generates Persistent OCD-Like Behavior , 2013, Science.

[207]  Fedde K. Potjer.  Region Adjacency Graphs and Connected Morphological Operators , 1996, ISMM.

[208]  Michael Grass,et al.  Comparison of Deep Learning Approaches for Multi-Label Chest X-Ray Classification , 2018, Scientific Reports.

[209]  David Myers,et al.  Atypical Breast Hyperplasia , 2019 .

[210]  A F W van der Steen,et al.  Plaque at RISK (PARISK): Prospective Multicenter Study to Improve Diagnosis of High-Risk Carotid Plaques , 2014, International journal of stroke : official journal of the International Stroke Society.

[211]  Dinggang Shen,et al.  Estimation of shape and growth brain network atlases for connectomic brain mapping in developing infants , 2018, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).

[212]  Tom Bourne,et al.  Dating and growth in the first trimester. , 2009, Best practice & research. Clinical obstetrics & gynaecology.

[213]  William M. Wells,et al.  Probabilistic Image Registration via Deep Multi-class Classification: Characterizing Uncertainty , 2019, UNSURE/CLIP@MICCAI.

[214]  J. Gilmore,et al.  Infant Brain Atlases from Neonates to 1- and 2-Year-Olds , 2011, PloS one.

[215]  Michael Milford,et al.  Evaluating Merging Strategies for Sampling-based Uncertainty Techniques in Object Detection , 2018, 2019 International Conference on Robotics and Automation (ICRA).

[216]  Bibek Dhital,et al.  Gibbs‐ringing artifact removal based on local subvoxel‐shifts , 2015, Magnetic resonance in medicine.

[217]  Wei Liu,et al.  Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images , 2018, ECCV.

[218]  Nicholas Ayache,et al.  Spectral Forests: Learning of Surface Data, Application to Cortical Parcellation , 2015, MICCAI.

[219]  Abhishek Kumar,et al.  Variational Inference of Disentangled Latent Concepts from Unlabeled Observations , 2017, ICLR.

[220]  Rushil Anirudh,et al.  Understanding Deep Neural Networks through Input Uncertainties , 2018, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[221]  J. S. Marron,et al.  A method for normalizing histology slides for quantitative analysis , 2009, 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[222]  Henrik Zetterberg,et al.  Traumatic brain injuries , 2016, Nature Reviews Disease Primers.

[223]  Konstantinos Kamnitsas,et al.  Efficient multi‐scale 3D CNN with fully connected CRF for accurate brain lesion segmentation , 2016, Medical Image Anal..

[224]  M. Breakspear,et al.  The connectomics of brain disorders , 2015, Nature Reviews Neuroscience.

[225]  Jean-Philippe Thiran,et al.  Structural connectomics in brain diseases , 2013, NeuroImage.

[226]  Michael I. Jordan,et al.  Learning Transferable Features with Deep Adaptation Networks , 2015, ICML.

[227]  Jaewoo Kang,et al.  Graph Transformer Networks , 2019, NeurIPS.

[228]  O. Sporns,et al.  Network neuroscience , 2017, Nature Neuroscience.

[229]  Evan M. Gordon,et al.  Local-Global Parcellation of the Human Cerebral Cortex From Intrinsic Functional Connectivity MRI , 2017, bioRxiv.

[230]  Max Welling,et al.  Semi-Supervised Classification with Graph Convolutional Networks , 2016, ICLR.

[231]  King-Sun Fu,et al.  A distance measure between attributed relational graphs for pattern recognition , 1983, IEEE Transactions on Systems, Man, and Cybernetics.

[232]  Farida Cheriet,et al.  Joint segmentation and classification of retinal arteries/veins from fundus images , 2019, Artif. Intell. Medicine.

[233]  Yong He,et al.  BrainNet Viewer: A Network Visualization Tool for Human Brain Connectomics , 2013, PloS one.

[234]  Rui Zheng,et al.  Simultaneous Arteriole and Venule Segmentation of Dual-Modal Fundus Images Using a Multi-Task Cascade Network , 2019, IEEE Access.

[235]  Yong-Sheng Chen,et al.  Pyramid Stereo Matching Network , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[236]  Andrew Gordon Wilson,et al.  Averaging Weights Leads to Wider Optima and Better Generalization , 2018, UAI.

[237]  Dinggang Shen,et al.  Hierarchical multi-atlas label fusion with multi-scale feature representation and label-specific patch partition , 2015, NeuroImage.

[238]  Lilian Calvet,et al.  Combining Visual Cues with Interactions for 3D–2D Registration in Liver Laparoscopy , 2020, Annals of Biomedical Engineering.

[239]  Ming Y. Lu,et al.  Pathomic Fusion: An Integrated Framework for Fusing Histopathology and Genomic Features for Cancer Diagnosis and Prognosis , 2019, IEEE Transactions on Medical Imaging.

[240]  Xiaojin Zhu,et al.  Semi-Supervised Learning , 2010, Encyclopedia of Machine Learning.

[241]  Li Yao,et al.  Weakly Supervised Medical Diagnosis and Localization from Multiple Resolutions , 2018, ArXiv.

[242]  C. Lehman,et al.  Diagnostic Accuracy of Digital Screening Mammography With and Without Computer-Aided Detection. , 2015, JAMA internal medicine.

[243]  Marisa O. Hollinshead,et al.  The organization of the human cerebral cortex estimated by intrinsic functional connectivity. , 2011, Journal of neurophysiology.

[244]  Jonathan Tompson,et al.  Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimation , 2014, NIPS.

[245]  Emmanuel Müller,et al.  NetLSD: Hearing the Shape of a Graph , 2018, KDD.

[246]  Nassir Navab,et al.  Learning to detect anatomical landmarks of the pelvis in X-rays from arbitrary views , 2019, International Journal of Computer Assisted Radiology and Surgery.

[247]  Li Shen,et al.  End-to-end Training for Whole Image Breast Cancer Diagnosis using An All Convolutional Design , 2017, ArXiv.

[248]  Yoshua Bengio,et al.  Convolutional neural networks for mesh-based parcellation of the cerebral cortex , 2018 .

[249]  Sébastien Ourselin,et al.  Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks , 2018, Neurocomputing.

[250]  Qiao Hu,et al.  Automated Separation of Binary Overlapping Trees in Low-Contrast Color Retinal Images , 2013, MICCAI.

[251]  Risi Kondor,et al.  The Multiscale Laplacian Graph Kernel , 2016, NIPS.

[252]  Ross B. Girshick,et al.  Focal Loss for Dense Object Detection , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[253]  Alexander M. Bronstein,et al.  Deformable Shape Completion with Graph Convolutional Autoencoders , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[254]  Islem Rekik,et al.  Morphological Brain Age Prediction using Multi-View Brain Networks Derived from Cortical Morphology in Healthy and Disordered Participants , 2019, Scientific Reports.

[255]  Thomas F. Coleman,et al.  A Subspace, Interior, and Conjugate Gradient Method for Large-Scale Bound-Constrained Minimization Problems , 1999, SIAM J. Sci. Comput..

[256]  Nico Karssemeijer,et al.  Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation , 2017, MICCAI.

[257]  M. Chun,et al.  Functional connectome fingerprinting: Identifying individuals based on patterns of brain connectivity , 2015, Nature Neuroscience.

[258]  Alan Connelly,et al.  Robust determination of the fibre orientation distribution in diffusion MRI: Non-negativity constrained super-resolved spherical deconvolution , 2007, NeuroImage.

[259]  Ulrik Brandes,et al.  On variants of shortest-path betweenness centrality and their generic computation , 2008, Soc. Networks.

[260]  Ben Glocker,et al.  Distance Metric Learning Using Graph Convolutional Networks: Application to Functional Brain Networks , 2017, MICCAI.

[261]  Tobias Ortmaier,et al.  Semantic denoising autoencoders for retinal optical coherence tomography , 2019, European Conference on Biomedical Optics.

[262]  Federico Cabitza,et al.  Who wants accurate models? Arguing for a different metrics to take classification models seriously , 2019, ArXiv.

[263]  Jens Rittscher,et al.  Towards the Identification of Histology Based Subtypes in Prostate Cancer , 2019, 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019).

[264]  Fei Wang,et al.  Deep learning for healthcare: review, opportunities and challenges , 2018, Briefings Bioinform..

[265]  Ulrike von Luxburg,et al.  A tutorial on spectral clustering , 2007, Stat. Comput..

[266]  Joseph V. Hajnal,et al.  Segmentation of Myelin-like Signals on Clinical MR Images for Age Estimation in Preterm Infants , 2018, bioRxiv.

[267]  Philip David,et al.  Domain Adaptation for Semantic Segmentation of Urban Scenes , 2017 .

[268]  Olaf Hellwich,et al.  Cell nuclei attributed relational graphs for efficient representation and classification of gastric cancer in digital histopathology , 2016, SPIE Medical Imaging.

[269]  Ralph C. Smith,et al.  Uncertainty Quantification: Theory, Implementation, and Applications , 2013 .

[270]  José Ignacio Orlando,et al.  U2-Net: A Bayesian U-Net Model With Epistemic Uncertainty Feedback For Photoreceptor Layer Segmentation In Pathological OCT Scans , 2019, 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019).

[271]  Alex Kendall,et al.  What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? , 2017, NIPS.

[272]  Jan Sijbers,et al.  Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data , 2014, NeuroImage.

[273]  Tom Heskes,et al.  Practical Confidence and Prediction Intervals , 1996, NIPS.

[274]  Ghassan Hamarneh,et al.  Prediction of Brain Network Age and Factors of Delayed Maturation in Very Preterm Infants , 2017, MICCAI.

[275]  P. Bonacich.  TECHNIQUE FOR ANALYZING OVERLAPPING MEMBERSHIPS , 1972 .

[276]  Purang Abolmaesumi,et al.  Confidence Calibration and Predictive Uncertainty Estimation for Deep Medical Image Segmentation , 2019, IEEE Transactions on Medical Imaging.

[277]  Abhishek Das,et al.  Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).

[278]  Andrew L. Maas.  Rectifier Nonlinearities Improve Neural Network Acoustic Models , 2013 .

[279]  Lovedeep Gondara,et al.  Medical Image Denoising Using Convolutional Denoising Autoencoders , 2016, 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW).

[280]  Milan Sonka,et al.  Robust active appearance models and their application to medical image analysis , 2005, IEEE Transactions on Medical Imaging.

[281]  BoykovYuri,et al.  Graph Cuts and Efficient N-D Image Segmentation , 2006 .

[282]  Olaf Sporns,et al.  Graph theory methods: applications in brain networks , 2018, Dialogues in clinical neuroscience.

[283]  Dong-Hyun Lee,et al.  Pseudo-Label : The Simple and Efficient Semi-Supervised Learning Method for Deep Neural Networks , 2013 .

[284]  U. Rajendra Acharya,et al.  Deep learning for healthcare applications based on physiological signals: A review , 2018, Comput. Methods Programs Biomed..

[285]  Xavier Bresson,et al.  Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering , 2016, NIPS.

[286]  Gopinath Chennupati,et al.  On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks , 2019, NeurIPS.

[287]  Joan Bruna,et al.  Spectral Networks and Locally Connected Networks on Graphs , 2013, ICLR.

[288]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[289]  Bohyung Han,et al.  Learning for Single-Shot Confidence Calibration in Deep Neural Networks Through Stochastic Inferences , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[290]  Ming Y. Lu,et al.  Weakly Supervised Prostate Tma Classification Via Graph Convolutional Networks , 2019, 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI).

[291]  Ah Chung Tsoi,et al.  The Graph Neural Network Model , 2009, IEEE Transactions on Neural Networks.

[292]  Konstantinos Kamnitsas,et al.  Unsupervised domain adaptation in brain lesion segmentation with adversarial networks , 2016, IPMI.

[293]  Alessandro Vespignani,et al.  The social symbiome framework: Linking genes-to-global cultures in public health using network science , 2016 .

[294]  Ruoyu Li,et al.  Weakly Supervised Deep Learning for Thoracic Disease Classification and Localization on Chest X-rays , 2018, BCB.

[295]  Michael Kampffmeyer,et al.  Uncertainty and Interpretability in Convolutional Neural Networks for Semantic Segmentation of Colorectal Polyps , 2020, Medical Image Anal..

[296]  Nicolas Papadakis,et al.  GraphX$^{NET}-$ Chest X-Ray Classification Under Extreme Minimal Supervision , 2019, 1907.10085.

[297]  H. Eichenbaum,et al.  Interplay of Hippocampus and Prefrontal Cortex in Memory , 2013, Current Biology.

[298]  Dinggang Shen,et al.  Simulating deformations of MR brain images for validation of atlas-based segmentation and registration algorithms , 2006, NeuroImage.

[299]  Timothy F. Cootes,et al.  A benchmark for comparison of dental radiography analysis algorithms , 2016, Medical Image Anal..

[300]  Subhransu Maji,et al.  A Bayesian Perspective on the Deep Image Prior , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[301]  Stefano Ermon,et al.  Accurate Uncertainties for Deep Learning Using Calibrated Regression , 2018, ICML.

[302]  Alessandro Gozzi,et al.  Group-Wise Functional Community Detection through Joint Laplacian Diagonalization , 2014, MICCAI.

[303]  Claudia Lindner,et al.  Robust and Accurate Shape Model Matching Using Random Forest Regression-Voting. , 2015, IEEE transactions on pattern analysis and machine intelligence.

[304]  Jan Eric Lenssen,et al.  Fast Graph Representation Learning with PyTorch Geometric , 2019, ArXiv.

[305]  Aron K Barbey,et al.  Structural brain connectivity and cognitive ability differences: A multivariate distance matrix regression analysis , 2017, Human brain mapping.

[306]  Myunghee Cho Paik,et al.  Uncertainty quantification using Bayesian neural networks in classification: Application to biomedical image segmentation , 2020, Comput. Stat. Data Anal..

[307]  Dinggang Shen,et al.  Progressive Graph-Based Transductive Learning for Multi-modal Classification of Brain Disorder Disease , 2016, MICCAI.

[308]  Tuo Zhang,et al.  Inferring Group-Wise Consistent Multimodal Brain Networks via Multi-View Spectral Clustering , 2013, IEEE Transactions on Medical Imaging.

[309]  Harald Kittler,et al.  Descriptor : The HAM 10000 dataset , a large collection of multi-source dermatoscopic images of common pigmented skin lesions , 2018 .

[310]  Gang Li,et al.  Hierarchical Rough-to-Fine Model for Infant Age Prediction Based on Cortical Features , 2020, IEEE Journal of Biomedical and Health Informatics.

[311]  Carola-Bibiane Schönlieb,et al.  Two Cycle Learning: Clustering Based Regularisation for Deep Semi-Supervised Classification , 2020, ArXiv.

[312]  Ali Shokoufandeh,et al.  A Graph Representation and Similarity Measure for Brain Networks with Nodal Features , 2018, GRAIL/Beyond-MIC@MICCAI.

[313]  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.

[314]  Ariel D. Procaccia,et al.  Variational Dropout and the Local Reparameterization Trick , 2015, NIPS.