• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.557-560
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• 2023

In this paper, we introduce a pre-training method leveraging the capabilities of the Vision Transformer (ViT) for disease diagnosis in conventional Fundus images. Recognizing the need for effective representation learning in medical images, our method combines the Vision Transformer with a Masked Autoencoder to generate meaningful and pertinent image augmentations. During pre-training, the Masked Autoencoder produces an altered version of the original image, which serves as a positive pair. The Vision Transformer then employs contrastive learning techniques with this image pair to refine its weight parameters. Our experiments demonstrate that this dual-model approach harnesses the strengths of both the ViT and the Masked Autoencoder, resulting in robust and clinically relevant feature embeddings. Preliminary results suggest significant improvements in diagnostic accuracy, underscoring the potential of our methodology in enhancing automated disease diagnosis in fundus imaging.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.11
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• pp.235-244
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• 2017

This paper aims to design a system for psychological diagnosis in sandbox play by applying rule based expert system. Sandbox play is one of play therapy and it is a technique that can be combined with psychological diagnosis and treatment using sand and various kinds of figures. In this technique, we focus on psychological diagnostic factors and try to implement a system that automatically diagnoses psychological types when input values are given. Therefore, six kinds of language objects are set and the rules are created according to the types of figures, arrangement of figures, and production time in the sand box used as a reference element in the diagnosis method. In this system, it is assumed that the client recognizes the finished sandbox as a sensor device. Then, when the recognized state enters the input value, the rules based on the language object are ignited in order. Through this, the client is diagnosed with one of 26 types of psychology. As a result, the diagnostic process is simplified and automated. Accordingly, a more detailed psychological diagnosis and treatments are provided.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.1
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• pp.97-102
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• 2013

Objective: The aim of this study was to develop an automated computer-aided diagnostic system for diagnosis of thyroid cancer pattern in fine needle aspiration cytology (FNAC) microscopic images with high degree of sensitivity and specificity using statistical texture features and a Support Vector Machine classifier (SVM). Materials and Methods: A training set of 40 benign and 40 malignant FNAC images and a testing set of 10 benign and 20 malignant FNAC images were used to perform the diagnosis of thyroid cancer. Initially, segmentation of region of interest (ROI) was performed by region-based morphology segmentation. The developed diagnostic system utilized statistical texture features derived from the segmented images using a Gabor filter bank at various wavelengths and angles. Finally, the SVM was used as a machine learning algorithm to identify benign and malignant states of thyroid nodules. Results: The SVMachieved a diagnostic accuracy of 96.7% with sensitivity and specificity of 95% and 100%, respectively, at a wavelength of 4 and an angle of 45. Conclusion: The results show that the diagnosis of thyroid cancer in FNAC images can be effectively performed using statistical texture information derived with Gabor filters in association with an SVM.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.11
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• pp.1417-1423
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• 1999

Distribution system can experience the diverse events instantly and permanently. Also, it can experience high impedance fault or line drop under unbalanced situation, Accordingly, it is difficulty to identify the fault location because that data collected from distribution SCADA system may include uncertainty. This paper proposes an expert system, which can infer the faulted location the quickly and exactly for the diverse events in the distribution system. The expert system utilizes distribution SCADA function and collected data, especially, the monitoring mechanism for the normal open position switches is adopted newly in order to recognize the fault type exactly. Also, automated fault location diagnosis strategy is developed in order to minimize the spreading effect of fault obtained from the error of the system operator. The proposed strategy is implemented in C language. Especially, in order to prove the effectiveness of proposed expert system, the several scenario is simulated for the given model system. The real feeders are selected as model system for the simulation.

• Smart Structures and Systems
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• v.33 no.4
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• pp.313-323
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• 2024

Deterioration of buildings is one of the biggest problems in modern society, and the importance of a safety diagnosis for old buildings is increasing. Therefore, most countries have legal maintenance and safety diagnosis regulations. However, the reliability of the existing safety diagnostic processes is reduced because they involve subjective judgments in the data collection. In addition, unstructured tasks increase rework rates, which are time-consuming and not cost-effective. Therefore, This paper proposed the method that can calculate the safety grade of deterioration automatically. For this, a DNN structure is generated by using existing precision inspection data and precision safety diagnostic data, and an objective building safety grade is calculated by applying status evaluation data obtained with a UAV, a laser scanner, and reverse engineering 3D models. This automated process is applied to 20 old buildings, taking about 40% less time than needed for a safety diagnosis from the existing manual operation based on the same building area. Subsequently, this study compares the resulting value for the safety grade with the already existing value to verify the accuracy of the grade calculation process, constructing the DNN with high accuracy at about 90%. This is expected to improve the reliability of aging buildings in the future, saving money and time compared to existing technologies, improving economic efficiency.

• Journal of Korean Neurosurgical Society
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• v.46 no.4
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• pp.333-339
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• 2009

Objective : Few studies on the clinical spectrum of automated pressure-controlled discography (APCD)-defined positive discs have been reported to date. Thus, the present study was undertaken to analyze clinical parameters critical for diagnosis of discogenic pain and to correlate imaging findings with intradiscal pressures and pain responses in patients with APCD-positive discs. Methods : Twenty-three patients who showed APCD-positive discs were selected for analysis. CT discogram findings and the degrees of nuclear degeneration seen on MRI were analyzed in comparison to changes of intradiscal pressure that provoked pain responses; and clinical pain patterns and dynamic factors were evaluated in relation to pain provocation. Results : Low back pain (LBP), usually centralized, with diffuse leg pain was the most frequently reported pattern of pain in these patients. Overall, LBP was most commonly induced by sitting posture, however, standing was highly correlated with L5/S1 disc lesions (p<0.01). MRI abnormalities were statistically correlated with grading of CT discogram results (p<005); with most pain response observed in CT discogram Grades 3 and 4. Pain-provoking pressure was not statistically correlated with MRI grading. However, it was higher in Grade 3 than Grade 4. Conclusion : APCD-positive discs were demonstrated in patients reporting centralized low back pain with diffuse leg pain, aggravated by sitting and standing. MRI was helpful to assess the degree of nuclear degeneration, yet it could not guarantee exact localization of the painful discs. APCD was considered to be more useful than conventional discography for diagnosis of discogenic pain.

• Progress in Medical Physics
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• v.26 no.1
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• pp.52-58
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• 2015

The automated dental cavity detection program for a new concept intra-oral dental x-ray imaging device, an auxiliary diagnosis system, which is able to assist a dentist to identify dental caries in an early stage and to make an accurate diagnosis, was to be developed. The primary theory of the automatic dental cavity detection program is divided into two algorithms; one is an image segmentation skill to discriminate between a dental cavity and a normal tooth and the other is a computational method to analyze feature of an tooth image and take an advantage of it for detection of dental cavities. In the present study, it is, first, evaluated how accurately the DRLSE (Direct Regularized Level Set Evolution) method extracts demarcation surrounding the dental cavity. In order to evaluate the ability of the developed algorithm to automatically detect dental cavities, 7 tooth phantoms from incisor to molar were fabricated which contained a various form of cavities. Then, dental cavities in the tooth phantom images were analyzed with the developed algorithm. Except for two cavities whose contours were identified partially, the contours of 12 cavities were correctly discriminated by the automated dental caries detection program, which, consequently, proved the practical feasibility of the automatic dental lesion detection algorithm. However, an efficient and enhanced algorithm is required for its application to the actual dental diagnosis since shapes or conditions of the dental caries are different between individuals and complicated. In the future, the automatic dental cavity detection system will be improved adding pattern recognition or machine learning based algorithm which can deal with information of tooth status.

• Journal of Korean Neurosurgical Society
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• v.46 no.1
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• pp.16-22
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• 2009

Objective : This study was designed to investigate automated pressure-controlled discography (APCD) findings, to calculate the elastance of intervertebral discs, and to assess the relationship between the calculated elastance and disc degeneration. Methods : APCD was performed in 19 patients. There were a total of 49 intervertebral discs treated. Following intradiscal puncture, a dye was constantly injected and the intradiscal pressure was continuously measured. The elastance of the intervertebral disc was defined as unit change in intradiscal pressure per fractional change in injected dye volume. Disc degeneration was graded using a modified Dallas discogram scale. Results : The mean elastance was 43.0${\pm}$9.6 psi/mL in Grade 0, 39.5${\pm}$8.3 psi/mL in Grade 1, 30.5${\pm}$22.3 psi/mL in Grade 2, 30.5${\pm}$22.3 psi/mL in Grade 3, 13.2${\pm}$8.3 psi/mL in Grade 4 and 6.9${\pm}$3.8 psi/mL in Grade 5. The elastance showed significant negative correlation with the degree of degeneration ($R^2$=0.529, P=0.000). Conclusion: APCD liberates the examiner from the data acquisition process during discography. This will likely improve the quality of data and the reliability of discography. Elastance could be used as an indicator of disc degeneration.

• Journal of Biomedical Engineering Research
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• v.17 no.1
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• pp.109-120
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• 1996

Cardiac function is evaluated quantitatively using angiographic images via the analysis of the shape change or the heart wall boundaries. To kin with, boundary defection or ESLV(End Systolic Lert Ventricular) and EDLV(End Diastolic Left Ventricular) is essential for the quantitative analysis of cardiac function. The boundary detection methods proposed in the past were almost semi-automatic. Intervention by a knowledgeable human operator was still required Of con, manual tracing of the boundaries is currently used for subsequent analysis and diagnosis. This method would not cut excessive time, labor, and subjectivity associated with manual intervention by a human operator. EDLV images have noncontiguous and ambiguous edge signal on some boundary regions. In this paper, we propose a new method for automated detection of boundaries in noncontiguous and ambiguous EDLV images. The boundary detection scheme which based on a priori knowledge information is divided into two steps. The first step is to detect the candidate edge points of EDLV using ESLV boundaries. The second step is to correct detected boundaries of EDLV using the LV shape. We developed the algorithm of modifying EDLV boundaries defined adaptive modifier. We experimented the method proposed in this paper and compared our proposed method with the manual method in detecting boundaries of EDLV. In the areas within estimated boundaries of EDLV, the percentage of error was about 1.4%. We verified the useflilness and obtained the satisfying results througll the experiments of the proposed method.

• Journal of the Korean Society for Nondestructive Testing
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• v.29 no.2
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• pp.145-152
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• 2009

Currently almost all in-service-inspection techniques, applied in domestic nuclear power plants, are partial to field inspection technique. These kinds of techniques are related to managing nuclear power plants by the operation of foreign-produced inspection devices. There have been so many needsfor development of native in-service-inspection device because there is no native diagnosis device for nuclear power plant inspection yet in Korea. In this research, we developed several core techniques to make an automated ultrasonic pipe inspection system for nuclear power plants. A high performance multi-channel ultrasonic pulser/receiver module, an A/D converter module and a digital main CPU module were developed and the performance of the developed modules was verified. The S/N ratio, noise level and signal acquisition performance of the developed modules showed proper level as we designed in the beginning.