• Journal of Intelligence and Information Systems
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• 제28권4호
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• pp.27-40
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• 2022

Medical AI, which has lately made significant advances, is playing a vital role, such as assisting clinicians with diagnosis and decision-making. The field of chest X-rays, in particular, is attracting a lot of attention since it is important for accessibility and identification of chest diseases, as well as the current COVID-19 pandemic. However, despite the vast amount of data, there remains a limit to developing an effective AI model due to a lack of labeled data. A research that used federated learning on chest X-ray data to lessen this difficulty has emerged, although it still has the following limitations. 1) It does not consider the problems that may occur in the Non-IID environment. 2) Even in the federated learning environment, there is still a shortage of labeled data of clients. We propose a method to solve the above problems by using the self-supervised learning model as a global model of federated learning. To that aim, we investigate a self-supervised learning methods suited for federated learning using chest X-ray data and demonstrate the benefits of adopting the self-supervised learning model for federated learning.

• Industry Promotion Research
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• 제7권3호
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• pp.17-26
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• 2022

As the well-being mindset to keep our minds and bodies free and healthy more than anything else in the society we live in is spreading, the meaning of health care has become a key part of the 4th industrial revolution such as big data, IoT, AI, and block chain. The advancement of the advanced medical information service industry is being promoted by utilizing convergence technology. In digital healthcare, the development of intelligent information technology such as artificial intelligence, big data, and cloud is being promoted as a digital transformation of the traditional medical and healthcare industry. In addition, due to rapid development in the convergence of science and technology environment, various issues such as health, medical care, welfare, etc., have been gradually expanded due to social change. Therefore, in this study, first, the general meaning and current status of digital health care medical information is examined, and then, developmental tasks to activate digital health care medical information are analyzed and reviewed. The purpose of this article is to improve usability to fully pursue our human freedom.

• Progress in Medical Physics
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• 제28권2호
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• pp.45-48
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• 2017

This paper evaluated the amount of radiation generated by wedge filters during radiation therapy using a high-energy linear accelerator, and the dose to the worker during wedge replacement. After 10-MV photon beam was irradiated with wedge filter, the wedge was removed from the linear accelerator, and the dose rate and energy spectrum were measured. The initial measurement was approximately 1 uSv/h, and the radiation level was reduced to 0.3 uSv/h after 6 min. The effective half-life derived from the dose rate measurement was approximately 3.5 min, and the influence of AI-28 was about 53%. From the energy spectrum measurements, a peak of 1,799 keV was measured for AI-28, while the peak for Co-58 was not measured in the control room. The peaks for Au-106 and Cd-105 were found only measurement was done without wedge removement from the linear accelerator. The additional doses received by the radiation worker during wedge replacement were estimated to be 0.08-0.4 mSv per year.

• Korean Journal of Artificial Intelligence
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• 제10권2호
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• pp.7-11
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• 2022

OLED displays cannot be used permanently due to burn-in or generation of dark spots due to degradation. Therefore, the time when the display can operate normally is very important. It is close to impossible to physically measure the time when the display operates normally. Therefore, the time that works normally should be predicted in a way other than a physical way. Therefore, if you do computer simulations based on artificial intelligence, you can increase the accuracy of prediction by saving time and continuous learning. Therefore, if we do computer simulations based on artificial intelligence, we can increase the accuracy of prediction by saving time and continuous learning. In this paper, a dataset in the form of development from generation to diffusion of dark spots, which is one of the causes related to the life of OLED, was generated by applying the finite element method. The dark spots were generated in nine conditions, such as 0.1 to 2.0 ㎛ with the size of pinholes, the number was 10 to 100, and 50% with water content. The learning data created in this way may be a criterion for generating an artificial intelligence-based dataset.

• Proceedings of the Korea Information Processing Society Conference
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• 한국정보처리학회 2023년도 춘계학술발표대회
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• pp.706-708
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• 2023

In this study, we embark on a journey to uncover the essence of emotions by exploring the depths of transfer learning on three pre-trained transformer models. Our quest to classify five emotions culminates in discovering the KLUE (Korean Language Understanding Evaluation)-BERT (Bidirectional Encoder Representations from Transformers) model, which is the most exceptional among its peers. Our analysis of F1 scores attests to its superior learning and generalization abilities on the experimental data. To delve deeper into the mystery behind its success, we employ the powerful SHAP (Shapley Additive Explanations) method to unravel the intricacies of the KLUE-BERT model. The findings of our investigation are presented with a mesmerizing text plot visualization, which serves as a window into the model's soul. This approach enables us to grasp the impact of individual tokens on emotion classification and provides irrefutable, visually appealing evidence to support the predictions of the KLUE-BERT model.

• Proceedings of the Korea Information Processing Society Conference
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• 한국정보처리학회 2023년도 춘계학술발표대회
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• pp.748-750
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• 2023

Facial expression recognition technology's rapid growth and development have garnered significant attention in recent years. This technology holds immense potential for various applications, making it crucial to stay up-to-date with the latest trends and advancements. Simultaneously, it is essential to identify and address the challenges that impede the technology's progress. Motivated by these factors, this study aims to understand the latest trends, future directions, and challenges in facial expression recognition technology by utilizing text mining to analyze papers published between 2020 and 2023. Our research focuses on discerning which aspects of these papers provide valuable insights into the field's recent developments and issues. By doing so, we aim to present the information in an accessible and engaging manner for readers, enabling them to understand the current state and future potential of facial expression recognition technology. Ultimately, our study seeks to contribute to the ongoing dialogue and facilitate further advancements in this rapidly evolving field.

• Korean Journal of Radiology
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• 제25권1호
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• pp.86-102
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• 2024

Early diagnosis, accurate assessment, and localization of peritoneal metastasis (PM) are essential for the selection of appropriate treatments and surgical guidance. However, available imaging modalities (computed tomography [CT], conventional magnetic resonance imaging [MRI], and 18fluorodeoxyglucose positron emission tomography [PET]/CT) have limitations. The advent of new imaging techniques and novel molecular imaging agents have revealed molecular processes in the tumor microenvironment as an application for the early diagnosis and assessment of PM as well as real-time guided surgical resection, which has changed clinical management. In contrast to clinical imaging, which is purely qualitative and subjective for interpreting macroscopic structures, radiomics and artificial intelligence (AI) capitalize on high-dimensional numerical data from images that may reflect tumor pathophysiology. A predictive model can be used to predict the occurrence, recurrence, and prognosis of PM, thereby avoiding unnecessary exploratory surgeries. This review summarizes the role and status of different imaging techniques, especially new imaging strategies such as spectral photon-counting CT, fibroblast activation protein inhibitor (FAPI) PET/CT, near-infrared fluorescence imaging, and PET/MRI, for early diagnosis, assessment of surgical indications, and recurrence monitoring in patients with PM. The clinical applications, limitations, and solutions for fluorescence imaging, radiomics, and AI are also discussed.

• Asian Pacific Journal of Cancer Prevention
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• 제14권1호
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• pp.281-285
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• 2013

Purpose: To probe the role of FasL in cell apoptosis in oral squamous cell carcinomas (OSCCs). Methods: The expression of Fas/FasL was assessed in 10 cases of normal oral epithelium, 38 cases of OSCC and tumor infiltrating lymphocytes (TIL), and 11 cases of metastatic lymph nodes by immunohistochemistry. Apoptosis of tumor cells and TIL was detected by terminal deoxynucleotidyl transferase-mediated dUTP-biotin nick end labeling assay (TUNEL). FasL-induction of T cell apoptosis was tested by co-culture assay in vitro with SCC-9 and Jurkat T cells. Results: The 10 cases of normal oral epithelium all demonstrated extensive expression of Fas, the positive rate being largely down-regulated in OSCC (21/38) (P<0.05) compared to the normal (10/10). At the same time, the positive rate of FasL significantly increased in OSCC (P<0.05) especially those with lymph node metastasis (P<0.05). The positive rates of Fas in well and middle differentiated OSCC were higher than those in poor differentiated OSCC (P<0.05). The AI of tumor cells in Fas-positive OSCC was remarkably higher than that in Fas-negative OSCC (P<0.01), with a positive correlation between Fas expression and cell differentiation as well as apoptosis (r=0.68, P<0.01). The AI of tumor cells in FasL positive OSCC was remarkably lower than that in control while the AI of TIL was higher than in FasL negative OSCC (P<0.05). The AI of tumor cells reversely correlated with that of TIL (r = -0. 72, P<0.05). It was found that SCC-9 cells expressing functional FasL could induce apoptosis of Jurkat cells as demonstrated by co-culture assays. As a conclusion, it is evident that OSCC cells expressing FasL can induce apoptosis in Fas-expressing T cells. Conclusions: In progression of OSCC, expression of the Fas/FasL changes significantly. The results suggest that FasL is a mediator of immune privilege in OSCC and may serve as an marker for predicting malignant change in oral tissues.

• Journal of KIBIM
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• 제13권3호
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• pp.12-20
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• 2023

Deep learning-based anomaly detection technology is used in various fields such as computer vision, speech recognition, and natural language processing. In particular, this technology is applied in various fields such as monitoring manufacturing equipment abnormalities, detecting financial fraud, detecting network hacking, and detecting anomalies in medical images. However, in the field of construction and architecture, research on deep learning-based data anomaly detection technology is difficult due to the lack of digitization of domain knowledge due to late digital conversion, lack of learning data, and difficulties in collecting and processing field data in real time. This study acquires necessary data through IoT (Internet of Things) from the viewpoint of monitoring for environmental management of architectural spaces, converts them into a database, learns deep learning, and then supports anomaly patterns using AI (Artificial Infelligence) deep learning-based anomaly detection. We propose an implementation process. The results of this study suggest an effective environmental anomaly pattern detection solution architecture for environmental management of architectural spaces, proving its feasibility. The proposed method enables quick response through real-time data processing and analysis collected from IoT. In order to confirm the effectiveness of the proposed method, performance analysis is performed through prototype implementation to derive the results.

• Journal of Intelligence and Information Systems
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• 제25권1호
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• pp.1-19
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• 2019

Artificial intelligence (AI) is one of the main driving forces leading the Fourth Industrial Revolution. The technologies associated with AI have already shown superior abilities that are equal to or better than people in many fields including image and speech recognition. Particularly, many efforts have been actively given to identify the current technology trends and analyze development directions of it, because AI technologies can be utilized in a wide range of fields including medical, financial, manufacturing, service, and education fields. Major platforms that can develop complex AI algorithms for learning, reasoning, and recognition have been open to the public as open source projects. As a result, technologies and services that utilize them have increased rapidly. It has been confirmed as one of the major reasons for the fast development of AI technologies. Additionally, the spread of the technology is greatly in debt to open source software, developed by major global companies, supporting natural language recognition, speech recognition, and image recognition. Therefore, this study aimed to identify the practical trend of AI technology development by analyzing OSS projects associated with AI, which have been developed by the online collaboration of many parties. This study searched and collected a list of major projects related to AI, which were generated from 2000 to July 2018 on Github. This study confirmed the development trends of major technologies in detail by applying text mining technique targeting topic information, which indicates the characteristics of the collected projects and technical fields. The results of the analysis showed that the number of software development projects by year was less than 100 projects per year until 2013. However, it increased to 229 projects in 2014 and 597 projects in 2015. Particularly, the number of open source projects related to AI increased rapidly in 2016 (2,559 OSS projects). It was confirmed that the number of projects initiated in 2017 was 14,213, which is almost four-folds of the number of total projects generated from 2009 to 2016 (3,555 projects). The number of projects initiated from Jan to Jul 2018 was 8,737. The development trend of AI-related technologies was evaluated by dividing the study period into three phases. The appearance frequency of topics indicate the technology trends of AI-related OSS projects. The results showed that the natural language processing technology has continued to be at the top in all years. It implied that OSS had been developed continuously. Until 2015, Python, C ++, and Java, programming languages, were listed as the top ten frequently appeared topics. However, after 2016, programming languages other than Python disappeared from the top ten topics. Instead of them, platforms supporting the development of AI algorithms, such as TensorFlow and Keras, are showing high appearance frequency. Additionally, reinforcement learning algorithms and convolutional neural networks, which have been used in various fields, were frequently appeared topics. The results of topic network analysis showed that the most important topics of degree centrality were similar to those of appearance frequency. The main difference was that visualization and medical imaging topics were found at the top of the list, although they were not in the top of the list from 2009 to 2012. The results indicated that OSS was developed in the medical field in order to utilize the AI technology. Moreover, although the computer vision was in the top 10 of the appearance frequency list from 2013 to 2015, they were not in the top 10 of the degree centrality. The topics at the top of the degree centrality list were similar to those at the top of the appearance frequency list. It was found that the ranks of the composite neural network and reinforcement learning were changed slightly. The trend of technology development was examined using the appearance frequency of topics and degree centrality. The results showed that machine learning revealed the highest frequency and the highest degree centrality in all years. Moreover, it is noteworthy that, although the deep learning topic showed a low frequency and a low degree centrality between 2009 and 2012, their ranks abruptly increased between 2013 and 2015. It was confirmed that in recent years both technologies had high appearance frequency and degree centrality. TensorFlow first appeared during the phase of 2013-2015, and the appearance frequency and degree centrality of it soared between 2016 and 2018 to be at the top of the lists after deep learning, python. Computer vision and reinforcement learning did not show an abrupt increase or decrease, and they had relatively low appearance frequency and degree centrality compared with the above-mentioned topics. Based on these analysis results, it is possible to identify the fields in which AI technologies are actively developed. The results of this study can be used as a baseline dataset for more empirical analysis on future technology trends that can be converged.