Name
Title | ||
|---|---|---|
Holistic Interstitial Lung Disease Detection using Deep Convolutional Neural Networks: Multi-label Learning and Unordered Pooling. |
Abstract | ||
|---|---|---|
Accurately predicting and detecting interstitial lung disease (ILD) patterns given any computed tomography (CT) slice without any pre-processing prerequisites, such as manually delineated regions of interest (ROIs), is a clinically desirable, yet challenging goal. The majority of existing work relies on manually-provided ILD ROIs to extract sampled 2D image patches from CT slices and, from there, performs patch-based ILD categorization. Acquiring manual ROIs is labor intensive and serves as a bottleneck towards fully-automated CT imaging ILD screening over large-scale populations. Furthermore, despite the considerable high frequency of more than one ILD pattern on a single CT slice, previous works are only designed to detect one ILD pattern per slice or patch. To tackle these two critical challenges, we present multi-label deep convolutional neural networks (CNNs) for detecting ILDs from holistic CT slices (instead of ROIs or sub-images). Conventional single-labeled CNN models can be augmented to cope with the possible presence of multiple ILD pattern labels, via 1) continuous-valued deep regression based robust norm loss functions or 2) a categorical objective as the sum of element-wise binary logistic losses. Our methods are evaluated and validated using a publicly available database of 658 patient CT scans under five-fold cross-validation, achieving promising performance on detecting four major ILD patterns: Ground Glass, Reticular, Honeycomb, and Emphysema. We also investigate the effectiveness of a CNN activation-based deep-feature encoding scheme using Fisher vector encoding, which treats ILD detection as spatially-unordered deep texture classification. |
Year | Venue | Field |
|---|---|---|
2017 | arXiv: Computer Vision and Pattern Recognition | Computer vision,Regression,Pattern recognition,Categorical variable,Computer science,Convolutional neural network,Pooling,Multi label learning,Interstitial lung disease,Computed tomography,Artificial intelligence,Encoding (memory) |
DocType | Volume | Citations |
Journal | abs/1701.05616 | 0 |
PageRank | References | Authors |
0.34 | 22 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mingchen Gao | 1 | 579 | 25.95 |
| Ziyue Xu | 2 | 597 | 35.50 |
| Le Lu | 3 | 1297 | 86.78 |
| Adam P. Harrison | 4 | 101 | 17.06 |
| Ronald M. Summers | 5 | 893 | 86.16 |
| Daniel J Mollura | 6 | 614 | 30.82 |