A joint multi-modal learning method for early-stage knee osteoarthritis disease classification

[1]  S. Ravalli,et al.  Exploiting real-world data to monitor physical activity in patients with osteoarthritis: the opportunity of digital epidemiology , 2022, Heliyon.

[2]  Yan Chai Hum,et al.  Emergence of Deep Learning in Knee Osteoarthritis Diagnosis , 2021, Comput. Intell. Neurosci..

[3]  A. Mobasheri,et al.  Early-stage symptomatic osteoarthritis of the knee — time for action , 2021, Nature Reviews Rheumatology.

[4]  Shaojie Tang,et al.  An Ensemble Hybrid Feature Selection Method for Neuropsychiatric Disorder Classification , 2021, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[5]  L. Sharma.  Osteoarthritis of the Knee. , 2021, The New England journal of medicine.

[6]  Jin Liu,et al.  Identification of early mild cognitive impairment using multi-modal data and graph convolutional networks , 2020, BMC Bioinformatics.

[7]  Wenxing Hu,et al.  Interpretable Multimodal Fusion Networks Reveal Mechanisms of Brain Cognition , 2020, IEEE Transactions on Medical Imaging.

[8]  R. Windhager,et al.  Predicting Total Knee Replacement from Symptomology and Radiographic Structural Change Using Artificial Neural Networks—Data from the Osteoarthritis Initiative (OAI) , 2020, Journal of clinical medicine.

[9]  Ying Zhuge,et al.  Automated Glioma Grading on Conventional MRI images Using Deep Convolutional Neural Networks. , 2020, Medical physics.

[10]  Aladine Chetouani,et al.  Discriminative Regularized Auto-Encoder for Early Detection of Knee OsteoArthritis: Data from the Osteoarthritis Initiative , 2020, IEEE Transactions on Medical Imaging.

[11]  F. Eckstein,et al.  Predicting knee replacement in participants eligible for disease-modifying osteoarthritis drug treatment with structural endpoints. , 2020, Osteoarthritis and cartilage.

[12]  H. Schaible,et al.  Impact of Diabetes Mellitus on Knee Osteoarthritis Pain and Physical and Mental Status: Data From the Osteoarthritis Initiative , 2020, Arthritis care & research.

[13]  Richard Kijowski,et al.  Deep learning risk assessment models for predicting progression of radiographic medial joint space loss over a 48-MONTH follow-up period. , 2020, Osteoarthritis and cartilage.

[14]  Simo Saarakkala,et al.  Multimodal Machine Learning-based Knee Osteoarthritis Progression Prediction from Plain Radiographs and Clinical Data , 2019, Scientific Reports.

[15]  E. Lespessailles,et al.  A decision support tool for early detection of knee OsteoArthritis using X-ray imaging and machine learning: Data from the OsteoArthritis Initiative , 2019, Comput. Medical Imaging Graph..

[16]  Yu-Ping Wang,et al.  Deep Collaborative Learning With Application to the Study of Multimodal Brain Development , 2019, IEEE Transactions on Biomedical Engineering.

[17]  T. Hastie,et al.  Modeling and predicting osteoarthritis progression: data from the osteoarthritis initiative. , 2018, Osteoarthritis and cartilage.

[18]  Yi Pan,et al.  An interpretable boosting model to predict side effects of analgesics for osteoarthritis , 2018, BMC Systems Biology.

[19]  M. Nevitt,et al.  Tool for osteoarthritis risk prediction (TOARP) over 8 years using baseline clinical data, X‐ray, and MRI: Data from the osteoarthritis initiative , 2018, Journal of magnetic resonance imaging : JMRI.

[20]  Bor-Sen Chen,et al.  Uncovering the regeneration strategies of zebrafish organs: a comprehensive systems biology study on heart, cerebellum, fin, and retina regeneration , 2018, BMC Systems Biology.

[21]  S. Bierma-Zeinstra,et al.  A machine learning approach for the identification of new biomarkers for knee osteoarthritis development in overweight and obese women. , 2017, Osteoarthritis and cartilage.

[22]  Qianjin Feng,et al.  Integrative Analysis of Histopathological Images and Genomic Data Predicts Clear Cell Renal Cell Carcinoma Prognosis. , 2017, Cancer research.

[23]  Simo Saarakkala,et al.  Automatic Knee Osteoarthritis Diagnosis from Plain Radiographs: A Deep Learning-Based Approach , 2017, Scientific Reports.

[24]  Wei Cao,et al.  Deep Learning based Radiomics (DLR) and its usage in noninvasive IDH1 prediction for low grade glioma , 2017, Scientific Reports.

[25]  Gholam-Ali Hossein-Zadeh,et al.  Structured and Sparse Canonical Correlation Analysis as a Brain-Wide Multi-Modal Data Fusion Approach , 2017, IEEE Transactions on Medical Imaging.

[26]  D Loeuille,et al.  Subchondral tibial bone texture analysis predicts knee osteoarthritis progression: data from the Osteoarthritis Initiative: Tibial bone texture & knee OA progression. , 2017, Osteoarthritis and cartilage.

[27]  Daoqiang Zhang,et al.  Joint Binary Classifier Learning for ECOC-Based Multi-Class Classification , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[28]  Yuan Yu,et al.  TensorFlow: A system for large-scale machine learning , 2016, OSDI.

[29]  Tianqi Chen,et al.  XGBoost: A Scalable Tree Boosting System , 2016, KDD.

[30]  Soo Beom Choi,et al.  Simple Scoring System and Artificial Neural Network for Knee Osteoarthritis Risk Prediction: A Cross-Sectional Study , 2016, PloS one.

[31]  N. Oreiro,et al.  Mitochondrial DNA (mtDNA) Haplogroups Influence the Progression of Knee Osteoarthritis. Data from the Osteoarthritis Initiative (OAI) , 2014, PloS one.

[32]  D. Felson,et al.  Identifying and treating preclinical and early osteoarthritis. , 2014, Rheumatic diseases clinics of North America.

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

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

[35]  Dinggang Shen,et al.  Inter-modality relationship constrained multi-modality multi-task feature selection for Alzheimer's Disease and mild cognitive impairment identification , 2014, NeuroImage.

[36]  T. Mosher,et al.  T2 texture index of cartilage can predict early symptomatic OA progression: data from the osteoarthritis initiative. , 2013, Osteoarthritis and cartilage.

[37]  A. Hofman,et al.  Prediction model for knee osteoarthritis incidence, including clinical, genetic and biochemical risk factors , 2013, Annals of the rheumatic diseases.

[38]  E. Vignon,et al.  In normal knees, joint space width (JSW) is correlated with the intermargin distance (IMD), a measure of medial tibial plateau alignment. Variations in IMD explain variability in JSW in serial radiographs. , 2013, Joint, bone, spine : revue du rhumatisme.

[39]  A. Hofman,et al.  Prediction model for knee osteoarthritis including clinical, genetic and biochemical risk factors , 2012 .

[40]  G. Stachowiak,et al.  Prediction of progression of radiographic knee osteoarthritis using tibial trabecular bone texture. , 2012, Arthritis and rheumatism.

[41]  G. Lester,et al.  The Osteoarthritis Initiative: A NIH Public–Private Partnership , 2012, HSS Journal.

[42]  Michael Doherty,et al.  Nottingham knee osteoarthritis risk prediction models , 2011, Annals of the rheumatic diseases.

[43]  Keith Lim,et al.  Perception is everything: OA is exciting , 2011, International journal of rheumatic diseases.

[44]  Daoqiang Zhang,et al.  Multimodal classification of Alzheimer's disease and mild cognitive impairment , 2011, NeuroImage.

[45]  William Mantovani,et al.  Pancreatic endocrine tumors: improved TNM staging and histopathological grading permit a clinically efficient prognostic stratification of patients , 2010, Modern Pathology.

[46]  Felix Eckstein,et al.  Quantitative MRI of cartilage and bone: degenerative changes in osteoarthritis , 2006, NMR in biomedicine.

[47]  R. McCarney,et al.  Knee pain and osteoarthritis in older adults: a review of community burden and current use of primary health care , 2001, Annals of the rheumatic diseases.

[48]  H K Genant,et al.  Trainable rule-based algorithm for the measurement of joint space width in digital radiographic images of the knee. , 2000, Medical physics.

[49]  D. Felson,et al.  An update on the epidemiology of knee and hip osteoarthritis with a view to prevention. , 1998, Arthritis and rheumatism.

[50]  D. Hawkes,et al.  Analysis of texture in macroradiographs of osteoarthritic knees using the fractal signature. , 1991, Physics in medicine and biology.

[51]  J. Kettenring,et al.  Canonical Analysis of Several Sets of Variables , 2022 .

[52]  Nigel Arden,et al.  Osteoarthritis: epidemiology. , 2006, Best practice & research. Clinical rheumatology.

[53]  David T Felson,et al.  An update on the pathogenesis and epidemiology of osteoarthritis. , 2004, Radiologic clinics of North America.

[54]  A. Woolf,et al.  Burden of major musculoskeletal conditions. , 2003, Bulletin of the World Health Organization.

[55]  J A Lynch,et al.  Analysis of texture in macroradiographs of osteoarthritic knees, using the fractal signature , 1991 .