Numerical solution of ruin probability of continuous time model based on optimal adaptive particle swarm optimization-triangular neural network algorithm

Xu, Yan; Fan, Xinyue; Wu, Jia

doi:10.1007/s00500-023-08602-1

Soft Comput

2023

DOI: 10.1007/s00500-023-08602-1

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Numerical solution of ruin probability of continuous time model based on optimal adaptive particle swarm optimization-triangular neural network algorithm

Yan Xu

Xinyue Fan

Jia Wu

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Cited by 4 publications

(1 citation statement)

References 36 publications

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“…If V = V s , do: low-frequency components of the image and preserves the high-frequency components of the image [53]. The Laplace operator is expressed as:…”

mentioning

confidence: 99%

Two‐stage coarse‐to‐fine method for pathological images in medical decision‐making systems

He,

Zhu,

et al. 2023

IET Image Processing

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Artificial intelligence decision systems play an important supporting role in the field of medical information. Medical image analysis is an important part of decision systems and an even more important part of medical diagnosis and treatment. The wealth of cellular information in histopathological images makes them a reliable means of diagnosing tumors. However, due to the large size, high resolution, and complex background structure of pathology images, deep learning methods still have various difficulties in the recognition of pathology images. Based on this, this study proposes a two‐stage continuous improvement‐based approach for pathology image recognition in medical decision systems. For pathology images with complex backgrounds, normalization and enhancement is performed to remove the effects of noise color, and light‐dark inconsistencies on the segmentation network. The continuous refinement PSP Net (CRPSPNet) is then designed for accurate recognition of the pathology images. CRPSPNet is divided into two stages: Pyramid Scene Parsing Network segmentation to obtain coarse segmentation results; and continuous refinement model refines the results of the first stage. Experiments using more than 1,000 osteosarcoma pathology images have shown that the method gives more accurate results with fewer computer resources and processing time than traditional optimization models. Its Intersection over Union achieves 0.76.

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“…If V = V s , do: low-frequency components of the image and preserves the high-frequency components of the image [53]. The Laplace operator is expressed as:…”

mentioning

confidence: 99%

Two‐stage coarse‐to‐fine method for pathological images in medical decision‐making systems

He,

Zhu,

et al. 2023

IET Image Processing

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Optimization of edge server group collaboration architecture strategy in IoT smart cities application

Gou,

2024

Peer-to-Peer Netw. Appl.

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Cytopathology image analysis method based on high-resolution medical representation learning in medical decision-making system

Li,

Liu,

et al. 2024

Complex Intell. Syst.

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Artificial intelligence has made substantial progress in many medical application scenarios. The quantity and complexity of pathology images are enormous, but conventional visual screening techniques are labor-intensive, time-consuming, and subject to some degree of subjectivity. Complex pathological data can be converted into mineable image features using artificial intelligence image analysis technology, enabling medical professionals to quickly and quantitatively identify regions of interest and extract information about cellular tissue. In this study, we designed a medical information assistance system for segmenting pathology images and quantifying statistical results, including data enhancement, cell nucleus segmentation, model tumor, and quantitative analysis. In cell nucleus segmentation, to address the problem of uneven healthcare resources, we designed a high-precision teacher model (HRMED_T) and a lightweight student model (HRMED_S). The HRMED_T model is based on visual Transformer and high-resolution representation learning. It achieves accurate segmentation by parallel low-resolution convolution and high-scaled image iterative fusion, while also maintaining the high-resolution representation. The HRMED_S model is based on the Channel-wise Knowledge Distillation approach to simplify the structure, achieve faster convergence, and refine the segmentation results by using conditional random fields instead of fully connected structures. The experimental results show that our system has better performance than other methods. The Intersection over the Union (IoU) of HRMED_T model reaches 0.756. The IoU of HRMED_S model also reaches 0.710 and params is only 3.99 M.

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Numerical solution of ruin probability of continuous time model based on optimal adaptive particle swarm optimization-triangular neural network algorithm

Cited by 4 publications

References 36 publications

Two‐stage coarse‐to‐fine method for pathological images in medical decision‐making systems

Two‐stage coarse‐to‐fine method for pathological images in medical decision‐making systems

Optimization of edge server group collaboration architecture strategy in IoT smart cities application

Cytopathology image analysis method based on high-resolution medical representation learning in medical decision-making system

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