DilatedFormer: dilated granularity transformer network for placental maturity grading in ultrasound

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.