Image Recognition and Analysis of Intrauterine Residues Based on Deep Learning and Semi-Supervised Learning

• Main Authors: Tao Tao, Kan Liu, Li Wang, Haiying Wu
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Access
• Subjects: Deep learning; semi-supervised learning; image recognition; intrauterine residue
• Online Access: https://ieeexplore.ieee.org/document/9180269/

Residual placenta is one of the common types of postpartum complications in clinical practice. Residual placenta is also the main and most common cause of late postpartum hemorrhage. This article proposes a spatial pyramid loop module, which solves the problem that the existing network structure cannot effectively extract the semantic information and category information in the image at the same time. The spatial pyramid structure is used to effectively extract the semantic information and category information. In addition, this article proposes to use cyclic convolutional network to realize the transfer function of information at different scales, and build it in the spatial pyramid structure to further strengthen the ability to extract semantic information and category information. This article proposes a feature fusion module to solve the impact of image classification network used in the base network in the existing network structure. The attention mechanism is used to achieve the effective fusion of high-dimensional features and low-dimensional features in the base network to reduce the influence of the base network, so as to better recover the recognition and prediction results. A semantic category loss function is proposed to supervise the categories of objects in images. This article builds it on the feature layer with the smallest scale, which not only increases the intermediate supervision to make the network fully converge, but also reduces the difficulty of extracting category information, and makes full use of the information transfer function of the cyclic convolutional network. This article introduces uncertainty information into the field of image segmentation to provide the accuracy of segmentation. For the purpose of uncertainty information, this article improves the network structure. At the same time for the image segmentation task, this article improves the Bayesian cross entropy loss function. The experiment verifies the necessity of improving the Bayesian crossover function in this article and the effectiveness of the conditional random field used in this article, and also verifies the effectiveness of the proposed semi-supervised learning method.