Data Transmission Strategy Based on Node Motion Prediction IoT System in Opportunistic Social Networks

Computational Intelligence and Neuroscience

et al. 2022

Self Cite

Osteosarcoma is one of the most common bone tumors that occurs in adolescents. Doctors often use magnetic resonance imaging (MRI) through biosensors to diagnose and predict osteosarcoma. However, a number of osteosarcoma MRI images have the problem of the tumor shape boundary being vague, complex, or irregular, which causes doctors to encounter difficulties in diagnosis and also makes some deep learning methods lose segmentation details as well as fail to locate the region of the osteosarcoma. In this article, we propose a novel boundary-aware grid contextual attention net (BA-GCA Net) to solve the problem of insufficient accuracy in osteosarcoma MRI image segmentation. First, a novel grid contextual attention (GCA) is designed to better capture the texture details of the tumor area. Then the statistical texture learning block (STLB) and the spatial transformer block (STB) are integrated into the network to improve its ability to extract statistical texture features and locate tumor areas. Over 80,000 MRI images of osteosarcoma from the Second Xiangya Hospital are adopted as a dataset for training, testing, and ablation studies. Results show that our proposed method achieves higher segmentation accuracy than existing methods with only a slight increase in the number of parameters and computational complexity.

Section: Boundary Prediction Enhancement Methodsmentioning

confidence: 99%

BA-GCA Net: Boundary-Aware Grid Contextual Attention Net in Osteosarcoma MRI Image Segmentation

Liu

Computational Intelligence and Neuroscience

et al. 2022

Self Cite

“…Medical image segmentation includes inputting digital greyscale images (i.e., CT or MRI) and outputting the predicted masks. e purpose is to extract information that can help with diagnoses from medical images, such as the possible position of the tumor, to reduce the amount of manual labor required and assist the physician in the diagnosis [23][24][25]. Various methods such as neural networks, decision trees, and Bayesian networks have been applied to receive the desired segmentation map output [26].…”

Section: Related Workmentioning

confidence: 99%

A Residual Fusion Network for Osteosarcoma MRI Image Segmentation in Developing Countries

Zhou

Computational Intelligence and Neuroscience

et al. 2022

Self Cite

Among primary bone cancers, osteosarcoma is the most common, peaking between the ages of a child’s rapid bone growth and adolescence. The diagnosis of osteosarcoma requires observing the radiological appearance of the infected bones. A common approach is MRI, but the manual diagnosis of MRI images is prone to observer bias and inaccuracy and is rather time consuming. The MRI images of osteosarcoma contain semantic messages in several different resolutions, which are often ignored by current segmentation techniques, leading to low generalizability and accuracy. In the meantime, the boundaries between osteosarcoma and bones or other tissues are sometimes too ambiguous to separate, making it a challenging job for inexperienced doctors to draw a line between them. In this paper, we propose using a multiscale residual fusion network to handle the MRI images. We placed a novel subnetwork after the encoders to exchange information between the feature maps of different resolutions, to fuse the information they contain. The outputs are then directed to both the decoders and a shape flow block, used for improving the spatial accuracy of the segmentation map. We tested over 80,000 osteosarcoma MRI images from the PET-CT center of a well-known hospital in China. Our approach can significantly improve the effectiveness of the semantic segmentation of osteosarcoma images. Our method has higher F1, DSC, and IOU compared with other models while maintaining the number of parameters and FLOPS.

“…In this section and the following section, we will describe how the proposed framework works in detail. The patterns in histopathological images are complex and require large amounts of labeled data to train the model [ 52 , 53 , 54 ]. In order not to increase the annotation burden, we constructed a new classification model that combines active learning and generative adversarial models, choosing a conditional variational self-encoder (CVAEGAN) as the main framework.…”

Section: System Designmentioning

confidence: 99%

A Multimodal Auxiliary Classification System for Osteosarcoma Histopathological Images Based on Deep Active Learning

Zhu

et al. 2022

Healthcare

Self Cite

Histopathological examination is an important criterion in the clinical diagnosis of osteosarcoma. With the improvement of hardware technology and computing power, pathological image analysis systems based on artificial intelligence have been widely used. However, classifying numerous intricate pathology images by hand is a tiresome task for pathologists. The lack of labeling data makes the system costly and difficult to build. This study constructs a classification assistance system (OHIcsA) based on active learning (AL) and a generative adversarial network (GAN). The system initially uses a small, labeled training set to train the classifier. Then, the most informative samples from the unlabeled images are selected for expert annotation. To retrain the network, the final chosen images are added to the initial labeled dataset. Experiments on real datasets show that our proposed method achieves high classification performance with an AUC value of 0.995 and an accuracy value of 0.989 using a small amount of labeled data. It reduces the cost of building a medical system. Clinical diagnosis can be aided by the system’s findings, which can also increase the effectiveness and verifiable accuracy of doctors.