Predicting Knee Osteoarthritis Progression from Structural MRI Using Deep Learning
暂无分享,去创建一个
Simo Saarakkala | Aleksei Tiulpin | Miika T. Nieminen | Egor Panfilov | A. Tiulpin | M. Nieminen | S. Saarakkala | E. Panfilov
[1] Cordelia Schmid,et al. ViViT: A Video Vision Transformer , 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
[2] Simo Saarakkala,et al. Multimodal Machine Learning-based Knee Osteoarthritis Progression Prediction from Plain Radiographs and Clinical Data , 2019, Scientific Reports.
[3] Junlong Zhong,et al. Global, regional prevalence, incidence and risk factors of knee osteoarthritis in population-based studies , 2020, EClinicalMedicine.
[4] S. Majumdar,et al. Deep learning for large scale MRI-based morphological phenotyping of osteoarthritis , 2021, Scientific Reports.
[5] Ahmet Gunduz,et al. Resource Efficient 3D Convolutional Neural Networks , 2019, 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW).
[6] Gregory Chang,et al. Total Knee Replacement prediction using Structural MRIs and 3D Convolutional Neural Networks , 2019 .
[7] M. Haapea,et al. Elevated adiabatic T1ρ and T2ρ in articular cartilage are associated with cartilage and bone lesions in early osteoarthritis: A preliminary study , 2017, Journal of magnetic resonance imaging : JMRI.
[8] Zhuowen Tu,et al. Aggregated Residual Transformations for Deep Neural Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[9] Tassilo Klein,et al. Multimodal Self-Supervised Learning for Medical Image Analysis , 2019, IPMI.
[10] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[11] J. Kellgren,et al. Radiological Assessment of Rheumatoid Arthritis * , 1957, Annals of the rheumatic diseases.
[12] Georg Heigold,et al. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale , 2021, ICLR.
[13] Sharmila Majumdar,et al. Deep Learning Predicts Total Knee Replacement from Magnetic Resonance Images , 2020, Scientific Reports.
[14] Peng Cao,et al. Learning osteoarthritis imaging biomarkers from bone surface spherical encoding , 2020, Magnetic resonance in medicine.
[15] Takaya Saito,et al. The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets , 2015, PloS one.
[16] Neil Houlsby,et al. Supervised Transfer Learning at Scale for Medical Imaging , 2021, ArXiv.
[17] Yann LeCun,et al. A Closer Look at Spatiotemporal Convolutions for Action Recognition , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[18] Huiye Liu,et al. TransFuse: Fusing Transformers and CNNs for Medical Image Segmentation , 2021, MICCAI.
[19] Akshay Pai,et al. One Network to Segment Them All: A General, Lightweight System for Accurate 3D Medical Image Segmentation , 2019, MICCAI.
[20] Shekoofeh Azizi,et al. Big Self-Supervised Models Advance Medical Image Classification , 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
[21] Chunhua Shen,et al. CoTr: Efficiently Bridging CNN and Transformer for 3D Medical Image Segmentation , 2021, MICCAI.
[22] Taghi M. Khoshgoftaar,et al. Survey on deep learning with class imbalance , 2019, J. Big Data.
[23] Jon Kleinberg,et al. Transfusion: Understanding Transfer Learning for Medical Imaging , 2019, NeurIPS.