Article ; Online: Unsupervised domain adaptation for the detection of cardiomegaly in cross-domain chest X-ray images
Frontiers in Artificial Intelligence, Vol
2023 Volume 6
Abstract: In recent years, several deep learning approaches have been successfully applied in the field of medical image analysis. More specifically, different deep neural network architectures have been proposed and assessed for the detection of various ... ...
| Abstract | In recent years, several deep learning approaches have been successfully applied in the field of medical image analysis. More specifically, different deep neural network architectures have been proposed and assessed for the detection of various pathologies based on chest X-ray images. While the performed assessments have shown very promising results, most of them consist in training and evaluating the performance of the proposed approaches on a single data set. However, the generalization of such models is quite limited in a cross-domain setting, since a significant performance degradation can be observed when these models are evaluated on data sets stemming from different medical centers or recorded under different protocols. The performance degradation is mostly caused by the domain shift between the training set and the evaluation set. To alleviate this problem, different unsupervised domain adaptation approaches are proposed and evaluated in the current work, for the detection of cardiomegaly based on chest X-ray images, in a cross-domain setting. The proposed approaches generate domain invariant feature representations by adapting the parameters of a model optimized on a large set of labeled samples, to a set of unlabeled images stemming from a different data set. The performed evaluation points to the effectiveness of the proposed approaches, since the adapted models outperform optimized models which are directly applied to the evaluation sets without any form of domain adaptation. |
|---|---|
| Keywords | chest X-ray ; cardiomegaly ; deep learning ; transfer learning ; unsupervised domain adaptation ; Electronic computers. Computer science ; QA75.5-76.95 |
| Subject code | 006 |
| Language | English |
| Publishing date | 2023-02-01T00:00:00Z |
| Publisher | Frontiers Media S.A. |
| Document type | Article ; Online |
| Database | BASE - Bielefeld Academic Search Engine (life sciences selection) |
Full text online
More links
Kategorien
Inter-library loan at ZB MED
Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.