| Autor: |
Yunzhu Wu, Yijun Yang, Lei Zhu, Zhenyan Han, Hong Luo, Xue Xue, Weiming Wang |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
Frontiers in Physics, Vol 11 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2296-424X |
| DOI: |
10.3389/fphy.2023.1239400 |
| Popis: |
Placental maturity grading (PMG) is often utilized for evaluating fetal growth and maternal health. Currently, PMG often relied on the subjective judgment of the clinician, which is time-consuming and tends to incur a wrong estimation due to redundancy and repeatability of the process. The existing methods often focus on designing diverse hand-crafted features or combining deep features and hand-crafted features to learn a hybrid feature with an SVM for grading the placental maturity of ultrasound images. Motivated by the dominated performance of end-to-end convolutional neural networks (CNNs) at diverse medical imaging tasks, we devise a dilated granularity transformer network for learning multi-scale global transformer features for boosting PMG. Our network first devises dilated transformer blocks to learn multi-scale transformer features at each convolutional layer and then integrates these obtained multi-scale transformer features for predicting the final result of PMG. We collect 500 ultrasound images to verify our network, and experimental results show that our network clearly outperforms state-of-the-art methods on PMG. In the future, we will strive to improve the computational complexity and generalization ability of deep neural networks for PMG. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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