Name
Title | ||
|---|---|---|
Fusing learned representations from Riesz Filters and Deep CNN for lung tissue classification. |
Abstract | ||
|---|---|---|
•A novel method to detect and classify several classes of diseased and healthy lung tissue in CT (Computed Tomography) images based on the fusion of Riesz and deep learning features is presented.•First, discriminative parametric lung tissue texture signatures are learned through Riesz representations using a one–versus–one approach.•Second, features from deep Convolutional Neural Networks (CNN) are computed by fine–tuning the GoogLeNet architecture using an augmented version of the same ILD dataset.•The two learned representations are combined in a joint softmax model for final classification, where early and late feature fusion schemes are compared.•The experimental results show that a late fusion of the independent probabilities leads to significant improvements in classification performance when compared to each of the separate feature representations. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.media.2019.06.006 | Medical Image Analysis |
Keywords | Field | DocType |
Texture signatures,Classification,ILD,Deep learning | Computer vision,Softmax function,Pattern recognition,Convolutional neural network,Medical imaging,Parametric statistics,Artificial intelligence,Invariant (mathematics),Deep learning,Discriminative model,Mathematics,Wavelet | Journal |
Volume | ISSN | Citations |
56 | 1361-8415 | 2 |
PageRank | References | Authors |
0.46 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ranveer Joyseeree | 1 | 4 | 2.51 |
| Sebastian Otálora | 2 | 10 | 3.07 |
| Henning Mueller | 3 | 39 | 3.52 |
| Adrien Depeursinge | 4 | 418 | 38.83 |