Name
Title | ||
|---|---|---|
SDFN: Segmentation-based Deep Fusion Network for Thoracic Disease Classification in Chest X-ray Images. |
Abstract | ||
|---|---|---|
•A novel CNN framework that utilized both holistic and local features of chest X-rays.•A segmentation model with additional post-processing were used to intelligently crop the local lung region image.•The developed framework consistently improved the AUCs of all 14 diseases compared to the state-of-art CheXnet.•Generated more accurate and reliable class activation maps compared to the existing methods. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.compmedimag.2019.05.005 | Computerized Medical Imaging and Graphics |
Keywords | Field | DocType |
Chest X-ray,Deep learning,Thoracic disease classification,Feature extraction,Feature fusion | Computer vision,Receiver operating characteristic,Domain knowledge,Convolutional neural network,Segmentation,Artificial intelligence,Artificial neural network,Discriminative model,Medicine,Image resolution,Network performance | Journal |
Volume | ISSN | Citations |
75 | 0895-6111 | 6 |
PageRank | References | Authors |
0.53 | 19 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Han Liu | 1 | 25 | 2.64 |
| Lei Wang | 2 | 947 | 61.46 |
| Yandong Nan | 3 | 6 | 0.53 |
| Faguang Jin | 4 | 6 | 0.53 |
| Jiantao Pu | 5 | 277 | 23.12 |