Deciphering multiple sclerosis disability with deep learning attention maps on clinical MRI

[1]  D. Goodin,et al.  Remote Observational Research for Multiple Sclerosis , 2022, Neurology: Neuroimmunology & Neuroinflammation.

[2]  S. Huffel,et al.  Classification of multiple sclerosis clinical profiles using machine learning and grey matter connectome , 2022, Frontiers in Robotics and AI.

[3]  M. Sormani,et al.  Heterogeneity on long-term disability trajectories in patients with secondary progressive MS: a latent class analysis from Big MS Data network , 2022, Journal of Neurology, Neurosurgery, and Psychiatry.

[4]  R. Henry,et al.  Differences in Age-related Retinal and Cortical Atrophy Rates in Multiple Sclerosis , 2022, Neurology.

[5]  H. Siebner,et al.  Linking lesions in sensorimotor cortex to contralateral hand function in multiple sclerosis: a 7 T MRI study , 2022, Brain : a journal of neurology.

[6]  M. Filippi,et al.  The role of cerebellar damage in explaining disability and cognition in multiple sclerosis phenotypes: a multiparametric MRI study , 2022, Journal of Neurology.

[7]  M. Filippi,et al.  A Deep Learning Approach to Predicting Disease Progression in Multiple Sclerosis Using Magnetic Resonance Imaging , 2022, Investigative radiology.

[8]  O. Ciccarelli,et al.  Spatial patterns of brain lesions assessed through covariance estimations of lesional voxels in multiple Sclerosis: The SPACE-MS technique , 2021, NeuroImage: Clinical.

[9]  O. Ciccarelli,et al.  Brain microstructural and metabolic alterations detected in vivo at onset of the first demyelinating event , 2021, Brain : a journal of neurology.

[10]  D. Shen,et al.  Multi-Task Weakly-Supervised Attention Network for Dementia Status Estimation With Structural MRI , 2021, IEEE Transactions on Neural Networks and Learning Systems.

[11]  May D. Wang,et al.  Multimodal deep learning models for early detection of Alzheimer’s disease stage , 2021, Scientific Reports.

[12]  J. Reichenbach,et al.  Investigation of Deep-Learning-Driven Identification of Multiple Sclerosis Patients Based on Susceptibility-Weighted Images Using Relevance Analysis , 2020, Frontiers in Neuroscience.

[13]  R. Deriche,et al.  Interpretable deep learning as a means for decrypting disease signature in multiple sclerosis , 2021, Journal of neural engineering.

[14]  Y. Lui,et al.  Harnessing Real-World Data to Inform Decision-Making: Multiple Sclerosis Partners Advancing Technology and Health Solutions (MS PATHS) , 2020, Frontiers in Neurology.

[15]  O. Commowick,et al.  Artificial intelligence to predict clinical disability in patients with multiple sclerosis using FLAIR MRI. , 2020, Diagnostic and interventional imaging.

[16]  O. Ciccarelli,et al.  Clinical relevance of cortical network dynamics in early primary progressive MS , 2019, Multiple sclerosis.

[17]  M. Battaglini,et al.  MAGNIMS consensus recommendations on the use of brain and spinal cord atrophy measures in clinical practice , 2020, Nature Reviews Neurology.

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

[19]  B. Fischl,et al.  FastSurfer - A fast and accurate deep learning based neuroimaging pipeline , 2019, NeuroImage.

[20]  Martin Weygandt,et al.  Layer-Wise Relevance Propagation for Explaining Deep Neural Network Decisions in MRI-Based Alzheimer's Disease Classification , 2019, Front. Aging Neurosci..

[21]  Giorgio Terracina,et al.  Classification of Multiple Sclerosis Clinical Profiles via Graph Convolutional Neural Networks , 2019, Front. Neurosci..

[22]  Doina Precup,et al.  Prediction of Disease Progression in Multiple Sclerosis Patients using Deep Learning Analysis of MRI Data , 2019, MIDL.

[23]  M. Brázdil,et al.  Impaired Self-Other Distinction and Subcortical Gray-Matter Alterations Characterize Socio-Cognitive Disturbances in Multiple Sclerosis , 2019, Front. Neurol..

[24]  Michael Scheel,et al.  Uncovering convolutional neural network decisions for diagnosing multiple sclerosis on conventional MRI using layer-wise relevance propagation , 2019, NeuroImage: Clinical.

[25]  E. D’Angelo,et al.  Default Mode Network Structural Integrity and Cerebellar Connectivity Predict Information Processing Speed Deficit in Multiple Sclerosis , 2019, Front. Cell. Neurosci..

[26]  David Bonekamp,et al.  Automated brain extraction of multisequence MRI using artificial neural networks , 2019, Human brain mapping.

[27]  S. Ourselin,et al.  Structural cortical network reorganization associated with early conversion to multiple sclerosis , 2018, Scientific Reports.

[28]  O. Ciccarelli,et al.  Multiple sclerosis , 2018, The Lancet.

[29]  Frederik Barkhof,et al.  Assessing treatment outcomes in multiple sclerosis trials and in the clinical setting , 2018, Nature Reviews Neurology.

[30]  M. Battaglini,et al.  Deep grey matter volume loss drives disability worsening in multiple sclerosis , 2017, bioRxiv.

[31]  M. Calabrese,et al.  Cortical Gray Matter MR Imaging in Multiple Sclerosis. , 2017, Neuroimaging clinics of North America.

[32]  David H. Miller,et al.  An abnormal periventricular magnetization transfer ratio gradient occurs early in multiple sclerosis , 2017, Brain : a journal of neurology.

[33]  M. P. van den Heuvel,et al.  Deep learning predictions of survival based on MRI in amyotrophic lateral sclerosis , 2016, NeuroImage: Clinical.

[34]  Sébastien Ourselin,et al.  A multi-time-point modality-agnostic patch-based method for lesion filling in multiple sclerosis , 2016, NeuroImage.

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

[36]  Alexander Binder,et al.  On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation , 2015, PloS one.

[37]  À. Rovira,et al.  Defining high, medium and low impact prognostic factors for developing multiple sclerosis. , 2015, Brain : a journal of neurology.

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

[39]  Thomas Brox,et al.  Striving for Simplicity: The All Convolutional Net , 2014, ICLR.

[40]  Fernando Calamante,et al.  Contralateral cerebello-thalamo-cortical pathways with prominent involvement of associative areas in humans in vivo , 2014, Brain Structure and Function.

[41]  Andrew Zisserman,et al.  Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps , 2013, ICLR.

[42]  Robert W Motl,et al.  Validation of patient determined disease steps (PDDS) scale scores in persons with multiple sclerosis , 2013, BMC Neurology.

[43]  Qiang Li,et al.  Altered Fronto-Striatal and Fronto-Cerebellar Circuits in Heroin-Dependent Individuals: A Resting-State fMRI Study , 2013, PloS one.

[44]  Bernhard Hemmer,et al.  An automated tool for detection of FLAIR-hyperintense white-matter lesions in Multiple Sclerosis , 2012, NeuroImage.

[45]  M. H. Parks,et al.  Reduced fronto-cerebellar functional connectivity in chronic alcoholic patients. , 2012, Alcoholism, clinical and experimental research.

[46]  À. Rovira,et al.  Brainstem lesions in clinically isolated syndromes , 2010, Neurology.

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

[48]  J. Lowe,et al.  Regional variations in the extent and pattern of grey matter demyelination in multiple sclerosis: a comparison between the cerebral cortex, cerebellar cortex, deep grey matter nuclei and the spinal cord , 2008, Journal of Neurology, Neurosurgery, and Psychiatry.

[49]  G. Siracusa,et al.  Cognitive assessment and quantitative magnetic resonance metrics can help to identify benign multiple sclerosis , 2008, Neurology.

[50]  À. Rovira,et al.  Baseline MRI predicts future attacks and disability in clinically isolated syndromes , 2006, Neurology.

[51]  T. Vollmer,et al.  Prevalence and treatment of spasticity reported by multiple sclerosis patients , 2004, Multiple sclerosis.

[52]  P. M. Matthews,et al.  Evidence of early cortical atrophy in MS , 2003, Neurology.

[53]  Stephen M. Smith,et al.  A global optimisation method for robust affine registration of brain images , 2001, Medical Image Anal..

[54]  J. Kurtzke Rating neurologic impairment in multiple sclerosis , 1983, Neurology.

[55]  Xutao Li,et al.  A Deep Learning Approach to Nightfire Detection based on Low-Light Satellite , 2021, Computer Science & Information Technology (CS & IT).