AUTHOR=Wu Yunzhu , Yang Yijun , Zhu Lei , Han Zhenyan , Luo Hong , Xue Xue , Wang Weiming TITLE=DilatedFormer: dilated granularity transformer network for placental maturity grading in ultrasound JOURNAL=Frontiers in Physics VOLUME=11 YEAR=2023 URL=https://www.frontiersin.org/journals/physics/articles/10.3389/fphy.2023.1239400 DOI=10.3389/fphy.2023.1239400 ISSN=2296-424X ABSTRACT=

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.