Secure data stream transmission method for cell pathological image storage system

Yin

et al. 2021

Self Cite

Three-dimensional (3D) medical images are prone to overlap, and there are some problems, such as low detection efficiency and inconsistent with the actual situation. Therefore, a 3D medical image surface reconstruction method based on data mining and machine learning is proposed. The 3D medical images were classified according to different ways, the information frame of 3D medical images was established and the surface overlapping information model of 3D images was given. Based on this information framework, the nonlinear function of overlapping area information of 3D medical images was constructed. The weight of the nonlinear function was used to calculate the input and output results of overlapping area information. Combined with the input mode of 3D medical image information, the error between the information output and the expected output was set. The nonlinear function weight of the overlapping area information of 3D medical images was modified by using the learning rate and the use time of the overlapping area information, and the influence factors of the overlapping information detection were obtained by increasing the situation terms, so as to complete the detection of the surface reconstruction information of 3D medical images. The experimental results show that

“…The data mining algorithm and machine learning algorithm can ensure the objective results and eliminate human factors. 16 (2) The error is small and the convergence is good.…”

Section: Detection Methods Of Overlapping Region Information In 3d Me...mentioning

confidence: 99%

Section: Detection Of Overlapped Area Information On 3d Medical Image...mentioning

confidence: 99%

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Research on 3D medical image surface reconstruction based on data mining and machine learning

Hua

Yin

et al. 2021

Self Cite

“…24 Although traditional machine learning is helpful for image segmentation, the effect is not satisfactory. 25 Although some achievements have been made, it is far from enough in terms of the current situation. When deep learning is developed, its effect is very obvious.…”

Section: Related Work and Backgroundmentioning

confidence: 99%

CA‐Unet++: An improved structure for medical CT scanning based on the Unet++ Architecture

et al. 2022

Self Cite

Currently, deep learning has become more and more mature in the field of medical image segmentation. Through using the computer, the deep learning models established can completely help doctors to perform medical image segmentation. Most of the current deep learning models are based on Unet. The U-shaped structure and the skip connection layer of Unet can effectively achieve precise image segmentation. However, for complicated images, the network structure of Unet is not sufficient enough. In response to this problem, some scholars have designed Unet++ by adding a denser skip connection layer to U-Net. Compared to U-Net, Unet++ is more effective in dealing with complex images, but it has drawbacks in many aspects, and there is still a large loss of eigenvalues in the skip connection and upsampling processes. To address these issues, this paper uses the channel and attention mechanism to improve the Unet++ model to obtain better image segmentation efficiency and accuracy. Meanwhile, based on Unet++, this paper designs a new model called CA-Unet++.The proposed model uses the channel module and the attention module to solve the eigenvalues loses in the long-distance skip connection process and the upsampling process, respectively. The experimental results and data analysis shows that our proposed CA-Unet++

“…For example, computer‐aided diagnosis (CAD) can help doctors to observe and process medical images to better treat patients. However, there are many interferences in medical images, traditional segmentation methods cannot obtain accurate segmentation results 6 . In this case, deep learning has attracted extensive attention in the domain of medical image segmentation.…”

Section: Introductionmentioning

confidence: 99%

RT‐Unet: An advanced network based on residual network and transformer for medical image segmentation

et al. 2022

Self Cite

For the past several years, semantic segmentation method based on deep learning, especially Unet, have achieved tremendous success in medical image processing. The U-shaped topology of Unet can well solve image segmentation tasks. However, due to the limitation of traditional convolution operations, Unet cannot realize global semantic information interaction. To address this problem, this paper proposes RT-Unet, which combines the advantages of Transformer and Residual network for accurate medical segmentation. In RT-Unet, the Residual block is taken as the image feature extraction layer to alleviate the problem of gradient degradation and obtain more effective features. Meanwhile, Skip-Transformer is proposed, which takes Multi-head Self-Attention as the main algorithm framework, instead of the original Skip-Connection layer in Unet to avoid the influence of shallow features on the network's performance. Besides, we add attention module at the decoder to reduce semantic differences. According to the experiments on MoNuSeg data set and ISBI_2018cell data set, RT-Unet achieves better segmentation performance than other deep learning-based algorithms. In addition, a series of further ablation experiments were conducted on Residual network and