Quantification of Lung Abnormalities in Cystic Fibrosis using Deep Networks
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Cystic fibrosis is a genetic disease which may appear in early life with structural abnormalities in lung tissues. We propose to detect these abnormalities using a texture classification approach. Our method is a cascade of two convolutional neural networks. The first network detects the presence of abnormal tissues. The second network identifies the type of the structural abnormalities: bronchiectasis, atelectasis or mucus plugging.We also propose a network computing pixel-wise heatmaps of abnormality presence learning only from the patch-wise annotations. Our database consists of CT scans of 194 subjects. We use 154 subjects to train our algorithms and the 40 remaining ones as a test set. We compare our method with random forest and a single neural network approach. The first network reaches an accuracy of 0,94 for disease detection, 0,18 higher than the random forest classifier and 0,37 higher than the single neural network. Our cascade approach yields a final class-averaged F1-score of 0,33, outperforming the baseline method and the single network by 0,10 and 0,12.
Original language | English |
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Title of host publication | Medical Imaging 2018 : Image Processing |
Number of pages | 7 |
Publisher | SPIE - International Society for Optical Engineering |
Publication date | 2018 |
Article number | 105741G |
DOIs | |
Publication status | Published - 2018 |
Event | SPIE Medical Imaging 2018 - Houston, United States Duration: 10 Feb 2018 → 15 Feb 2018 |
Conference
Conference | SPIE Medical Imaging 2018 |
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Land | United States |
By | Houston |
Periode | 10/02/2018 → 15/02/2018 |
Series | Proceedings of SPIE International Symposium on Medical Imaging |
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Volume | 10574 |
Bibliographical note
SPIE - Medical Imaging 2018: Image Processing
Links
- https://arxiv.org/pdf/1803.07991
Accepted author manuscript
ID: 202482614