• Journal of Biomedical Engineering Research
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• v.25 no.6
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• pp.575-580
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• 2004

In this work, a method is presented to find an anatomical site of a current source crudely in a standard brain using grand average of MEG data. Minimum norm estimation algorithm and truncated singular value decomposition were applied to calculate the distributed sources that can reproduce the measured signals. Grand average over all subjects was obtained from the transformed signals, which would be detected in a standard sensor plane by the obtained distributed current sources. In the simulation study, it was shown that the localized dipole using the grand average is consistent with the mean location of localized dipoles of all subjects within several mm even with large inter-individual differences of sensor positions. This result suggests that the mean location of low level signal source can be estimated as a dipole source in grand average and it was confirmed in the localization of the current source of N100m. when the localized dipole is registered on a standard brain. This result also suggests that the activity region obtained from grand average can be crudely estimated on a standard brain using the source location of the N100m as a reference point.

• Journal of Internet Computing and Services
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• v.23 no.3
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• pp.35-45
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• 2022

Even in recent years, treatment of first-aid patients is still often delayed due to a shortage of medical resources in marginalized areas. Research on automating the analysis of medical data to solve the problems of inaccessibility for medical services and shortage of medical personnel is ongoing. Computer vision-based medical inspection automation requires a lot of cost in data collection and labeling for training purposes. These problems stand out in the works of classifying lesion that are rare, or pathological features and pathogenesis that are difficult to clearly define visually. Anomaly detection is attracting as a method that can significantly reduce the cost of data collection by adopting an unsupervised learning strategy. In this paper, we propose methods for detecting abnormal images on chest X-RAY images as follows based on existing anomaly detection techniques. (1) Normalize the brightness range of medical images resampled as optimal resolution. (2) Some feature vectors with high representative power are selected in set of patch features extracted as intermediate-level from lesion-free images. (3) Measure the difference from the feature vectors of lesion-free data selected based on the nearest neighbor search algorithm. The proposed system can simultaneously perform anomaly classification and localization for each image. In this paper, the anomaly detection performance of the proposed system for chest X-RAY images of PA projection is measured and presented by detailed conditions. We demonstrate effect of anomaly detection for medical images by showing 0.705 classification AUROC for random subset extracted from the PadChest dataset. The proposed system can be usefully used to improve the clinical diagnosis workflow of medical institutions, and can effectively support early diagnosis in medically poor area.

• Journal of Internet of Things and Convergence
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• v.10 no.2
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• pp.1-15
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• 2024

The construction of smart communities is a new method and important measure to ensure the security of residential areas. In order to solve the problem of low accuracy in face recognition caused by distorting facial features due to monitoring camera angles and other external factors, this paper proposes the following optimization strategies in designing a face recognition network: firstly, a global graph convolution module is designed to encode facial features as graph nodes, and a multi-scale feature enhancement residual module is designed to extract facial keypoint features in conjunction with the global graph convolution module. Secondly, after obtaining facial keypoints, they are constructed as a directed graph structure, and graph attention mechanisms are used to enhance the representation power of graph features. Finally, tensor computations are performed on the graph features of two faces, and the aggregated features are extracted and discriminated by a fully connected layer to determine whether the individuals' identities are the same. Through various experimental tests, the network designed in this paper achieves an AUC index of 85.65% for facial keypoint localization on the 300W public dataset and 88.92% on a self-built dataset. In terms of face recognition accuracy, the proposed network achieves an accuracy of 83.41% on the IBUG public dataset and 96.74% on a self-built dataset. Experimental results demonstrate that the network designed in this paper exhibits high detection and recognition accuracy for faces in surveillance videos.

• The Korean Journal of Nuclear Medicine
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• v.39 no.1
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• pp.9-14
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• 2005

Purpose: Abnormal myocardial perfusion may be caused by ventricular preexcitation, but its location, extent, severity and correlation with accessory pathway (AP) are not established. We evaluated perfusion patterns on myocardial perfusion SPECT and location of AP in patients with WPW (Wolff-Parkinson-White) syndrome. Materials and Methods: Adenosine Tc-99m MIBI or Tl-201 myocardial perfusion SPECT was performed in 11 patients with WPW syndrome. Perfusion defects (PD) were compared to AP location based on ECG with Fitzpatrick's algorithm or electrophysiologic study and radiofrequency catheter ablation. Results: Patients had atypical chest discomfort or no symptom. Risk of coronary artery disease (CAD) was below 0.1 in 11 patients using the nomogram to estimate the probability of CAD. Coronary angiography was performed in 4 patients (mid-LAD 50% in one, normal in others). In 4 patients, AP localization was done by electrophysiologic study and radiofrequency catheter ablation (RFCA). Small to large extent ($11.0{\pm}8.5%$, range:$3{\sim}35%$) and mild to moderate severity ($-71{\pm}42.7%$, range:$-2l7{\sim}-39%$) of reversible (n=9) or fixed (n=1) perfusion defects were noted. One patient with right free wall (right lateral) AP showed normal. PD locations were variable following the location of AP. One patient with left lateral wall AP was followed 6 weeks after RFCA and showed significantly decreased PD on SPECT with successful ablation. Conclusion: Myocardial perfusion defect showed variable extent, severity and location in patients with WPW syndrome. Abnormal perfusion defect showed in most of all patients, but it did not seem to be correlated specifically with location of accessory pathway and coronary artery disease. Therefore myocardial perfusion SPECT should be interpreted carefully in patients with WPW syndrome.

• Journal of the Korean Society of Radiology
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• v.83 no.2
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• pp.344-359
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• 2022

Purpose To develop a denoising convolutional neural network-based image processing technique and investigate its efficacy in diagnosing breast cancer using low-dose mammography imaging. Materials and Methods A total of 6 breast radiologists were included in this prospective study. All radiologists independently evaluated low-dose images for lesion detection and rated them for diagnostic quality using a qualitative scale. After application of the denoising network, the same radiologists evaluated lesion detectability and image quality. For clinical application, a consensus on lesion type and localization on preoperative mammographic examinations of breast cancer patients was reached after discussion. Thereafter, coded low-dose, reconstructed full-dose, and full-dose images were presented and assessed in a random order. Results Lesions on 40% reconstructed full-dose images were better perceived when compared with low-dose images of mastectomy specimens as a reference. In clinical application, as compared to 40% reconstructed images, higher values were given on full-dose images for resolution (p < 0.001); diagnostic quality for calcifications (p < 0.001); and for masses, asymmetry, or architectural distortion (p = 0.037). The 40% reconstructed images showed comparable values to 100% full-dose images for overall quality (p = 0.547), lesion visibility (p = 0.120), and contrast (p = 0.083), without significant differences. Conclusion Effective denoising and image reconstruction processing techniques can enable breast cancer diagnosis with substantial radiation dose reduction.