Name
Title | ||
|---|---|---|
Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learning. |
Abstract | ||
|---|---|---|
•An algorithm to automatically detect motion artefacts in cardiac MR short axis images.•Use of k-space corruption to generate realistic motion artefacts to address data imbalance.•Training of a spatio-temporal convolutional neural network using curriculum learning.•Detailed comparison against a range of machine learning methods.•An area under ROC curve of 0.89 is achieved. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.media.2019.04.009 | Medical Image Analysis |
Keywords | Field | DocType |
Cardiac MR motion artefacts,Image quality assessment,Artifact,Convolutional neural networks,LSTM | Population,k-space,Pattern recognition,Convolutional neural network,Computer science,Image quality,Cardiac magnetic resonance,Curriculum,Artificial intelligence,Deep learning,Area under the roc curve | Journal |
Volume | ISSN | Citations |
55 | 1361-8415 | 5 |
PageRank | References | Authors |
0.46 | 0 | 11 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ilkay Öksüz | 1 | 54 | 9.32 |
| Bram Ruijsink | 2 | 13 | 4.67 |
| Esther Puyol-Antón | 3 | 18 | 5.52 |
| James R. Clough | 4 | 17 | 5.79 |
| Gastao Cruz | 5 | 12 | 3.92 |
| Aurélien Bustin | 6 | 16 | 3.89 |
| Claudia Prieto | 7 | 30 | 9.15 |
| René Botnar | 8 | 19 | 4.24 |
| Daniel Rueckert | 9 | 9338 | 637.58 |
| Julia A Schnabel | 10 | 1978 | 151.49 |
| Andrew P. King | 11 | 483 | 59.98 |