Convolutional long and short time memory network is a kind of fusion model, which inherits the excellent spatial feature extraction ability of convolutional neural network, and can effectively complete the processing ...
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Mining rich semantic information hidden in heterogeneous information network is one of the important tasks of data mining. Generally, a nuclear medicine text consists of the description of disease (i.e., lesions) and ...
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Mining rich semantic information hidden in heterogeneous information network is one of the important tasks of data mining. Generally, a nuclear medicine text consists of the description of disease (i.e., lesions) and diagnostic results. However, how to construct a computer-aided diagnostic model with a large number of medical texts is a challenging task. To automatically diagnose diseases with SPECT imaging, in this work, we create a knowledge-based diagnostic model by exploring the association between a disease and its properties. Firstly, an overview of nuclear medicine and data mining is presented. Second, the method of preprocessing textual nuclear medicine diagnostic reports is proposed. Last, the created diagnostic modes based on random forest and SVM are proposed. Experimental evaluation conducted real-world data of diagnostic reports of SPECT imaging demonstrates that our diagnostic models are workable and effective to automatically identify diseases with textual diagnostic reports.
The rapid development of the Internet has brought convenience to people and has also produced the problem of "information overload". In view of the traditional collaborative filtering algorithm facing some b...
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Whole body bone scan image analysis is widely used in nuclear medicine to assist nuclear medicine physicians in the detection of bone metastases. At present, the analysis of whole-body bone scan images mainly relies o...
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SPECT lung perfusion is an important functional imaging technology. It can capture the functional lesions of the lung in a non-invasive manner and has become an important clinical detection method for diseases such as...
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Nuclear medicine SPECT is the main functional imaging modality, which plays an important role in the diagnosis and treatment of thyroid diseases. Hyperthyroidism is a common thyroid disease with symptoms such as exoph...
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Adjacent vertex distinguishing incidence coloring of graph G is an incidence coloring that satisfies that adjacent vertices have different color sets, and the minimum color number is called the adjacent vertex disting...
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An acyclic coloring of a graph is a proper vertex coloring such that there are no bichromatic cycles. The acyclic chromatic number of G, denoted a(G), is the minimum number of colors required for acyclic coloring of a...
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In this paper, we study periodic and S-asymptotically periodic solutions for fractional diffusion equations (FDE). As we all know, there is no exact periodic solution to differential equations with Caputo or Riemann-L...
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SPECT bone imaging is an important means to assist doctors in diagnosing diseases. The traditional processing method is that radiologists diagnose images. Manual diagnosis is not only cumbersome and time-consuming, bu...
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ISBN:
(数字)9781728190044
ISBN:
(纸本)9781728190051
SPECT bone imaging is an important means to assist doctors in diagnosing diseases. The traditional processing method is that radiologists diagnose images. Manual diagnosis is not only cumbersome and time-consuming, but also different diagnosis results will be caused by the different diagnosis experience of doctors. In view of the above problems, this paper uses U-Net network as the basic model, and at the same time conducts model performance optimization research. Based on the U-Net network, the attention mechanism is integrated to segment the bone metastases in the pelvic area. Introducing the attention mechanism into the U-Net network can help improve the correlation of the pelvic region and reduce the interference caused by problems such as uneven brightness and low contrast to the model. Through multiple sets of experimental demonstrations, the U-Net network integrated with the attention mechanism can better segment bone metastases in the pelvic region based on SPECT images, and the model indicators have been significantly improved.
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