• Asian Pacific Journal of Cancer Prevention
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• v.15 no.3
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• pp.1327-1331
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• 2014

Background: Electrical impedance tomography (EIT) is a new non-invasive, mobile screening method which does not use ionizing radiation to the human breast; allows conducting quantitative assessment of the images besides the visual interpretation. The aim of this study was to correlate the quantitative assessment and visual interpretation of breast electrical impedance tomographs and associated factors. Materials and Methods: One hundred and fifty mammography patients above 40 years and undergoing EIT were chosen using convenient sampling. Visual interpretation of the images was carried out by a radiologist with minimum of three years experience using the breast imaging - electrical impedance (BI-EIM) classification for detection of abnormalities. A set of thirty blinded EIT images were reinterpreted to determine the intra-rater reliability using kappa. Quantitative assessment was by comparison of the breast average electric conductivity with the norm and correlations with visual interpretation of the images were determined using Chi-square. One-way ANOVA was used to compare the mean electrical conductivity between groups and t-test was used for comparisons with pre-existing Caucasians statistics. Independent t-tests were applied to compare the mean electrical conductivity of women with factors like exogenous hormone use and family history of breast cancer. Results: The mean electrical conductivity of Malaysian women was significantly lower than that of Caucasians (p<0.05). Quantitative assessment of electrical impedance tomography was significantly related with visual interpretation of images of the breast (p<0.05). Conclusions: Quantitative assessment of electrical impedance tomography images was significantly related with visual interpretation.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.503-508
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• 1992

A new method for making automatic electroencephalogram(EEG) report based on the automatic quantitative interpretation of awake EEG was developed. We first analysed a. relationship between EEG reports and quantitative EEG interpretation done by a qualified electroencephalographer(EEGer) for 22 subjects. Based on the analysed relationship and usual process of report making by the EEGer, we defined all terminology necessary for EEG report and established rules for EEG report making. By the combined use of the proposed EEG report making and the method for automatic quantitative EEG interpretation presented at '90 KACC, we were able to make the automatic EEG reports which were equivalent to the EEG reports written by the EEGer. As all the procedures were programmed in a personal computer equipped with an AD (analogue-to-digital) converter, the automatic EEG reports were obtained in almost real time in usual actual EEG recording situation with only a few seconds time lag for the analysis in the computer. The proposed report making method and the quantitative EEG interpretation method will be effectively applicable to the clinical use as an assistant tool for physicians.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.1190-1195
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• 1990

A new system for automatic interpretation of the awake electroencephalogram(EEG) was developed in this work. We first clarified all the necessary items for EEG interpretation in accordance with an analysis of visual inspection of the rhythms by a qualified electroencephalographer (EEGer), and then defined each item quantitatively. Concerning the automatic interpretation, we made an effort to find out specific EEG parameters which faithfully represent the procedure of visual interpretation by the qualified EEGer. Those specific EEG parameters were calculated from periodograms of the EEG time series. By using EEG data of 14 subjects, the automatic EEG interpretation system was constructed and compared with the visual interpretation done by the EEGer. The automatic EEG interpretation thus established was proved to be in agreement with the visual interpretation by the EEGer.

• Education of Primary School Mathematics
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• v.17 no.2
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• pp.113-125
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• 2014

The purpose of the study is to examine how the 6th graders interpret graphs, and on basis of the research, to seek for guidance on ways to improve their analysis capabilities. All the students from two classes of D elementary school in Busan became the target to examine how to interpret graphs. On the basis of the result, 6 students who characterized by graph interpretation got an in-depth interview and the outcome was analyzed in detail. The students are able to understand both quantitative and qualitative meaning of graphs and they learned practicality while they think of graphs connecting with real life, most of all they have been interested in interpreting the meaning of graphs.

• Geophysics and Geophysical Exploration
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• v.8 no.1
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• pp.112-117
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• 2005

Geophysical logging is routinely undertaken as part of most coal mine exploration programs. Currently, the main application for the logs is to determine coal seam depth and to qualitatively estimate coal quality, lithology, and rock strength. However, further information can be obtained, if quantitative log interpretation is made. To assist in the uptake of quantitative interpretation, we discuss log responses in terms of the mineralogy of the clastic sedimentary rocks frequently found in the Australian black coal mining areas of the Sydney and Bowen Basins. We find that the log responses can be tied to the mineralogy with reasonable confidence. Ambiguities in the interpretation will be better resolved if a full suite of logs is run. A method for checking for internal consistency, by comparing calculated and observed velocities, is also described. A key driver for quantitative interpretation is geotechnical characterisation. We propose a classification system for clastic rocks that takes into consideration physical rock properties that can be inferred from geophysical logs.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.19-23
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• 1996

A full automatic interpretation of awake electroencephalogram (EEG) had been developed by the authors and presented at the past KACCs in series. The automatic EEG interpretation consists of four main parts: quantitative EEG interpretation, EEG report making, preprocessing of EEG data and adaptable EEG interpretation. The automatic EEG interpretation reveals essentially the same findings as the electroencephalographer's (EEG's), and then would be applicable in clinical use as an assistant tool for EEGer. The method had been developed through collaboration works between the engineering field (Saga University) and the medical field (Kyoto University). This work can be understood as an artificial realization of human expert skill. The procedure for the artificial realization was summarized in a methodology for artificial realization of human skill which will be applicable in other fields of systems control.

• Journal of the Korean Society of Radiology
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• v.82 no.4
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• pp.808-816
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• 2021

Pulmonary emphysema is a cause of chronic obstructive pulmonary disease. Emphysema can be accurately diagnosed via CT. The severity of emphysema can be assessed using visual interpretation or quantitative analysis. Various studies on emphysema using deep learning have also been conducted. Although the classification of emphysema has proven clinically useful, there is a need to improve the reliability of the measurement.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.505-508
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• 1995

The automatic interpretation of awake background electroencephalogram (EEG), consisting of quantitative EEG interpretation and EEG report making, has been developed by the authors based on EEG data visually inspected by an electroencephalographer (EEGer). The present study was focused on the adaptability of the automatic EEG interpretation which was accomplished by the constructive neural network with forgetting factor. The artificial neural network (ANN) was constructed so as to give the integrative decision of the EEG by using the input signals of the intermediate judgment of 13 items of the EEG. The feature of the ANN was that it adapted to any EEGer who gave visual inspection for the training data. The developed method was evaluated based on the EEG data of 57 patients. The re-trained ANN adapted to another EEGer appropriately.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.786-791
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• 2002

Mapping systems using Satellite Imagery has not been well-established compare to conventional Arial Photograph mapping systems. In order for satellite imagery to produce a stable quality of maps, it requires to follow the standard mapping procedures. In this satellite imagery study, we proposed four methods of mapping procedures. Mapping methods were established by generating trial maps and analyzing types of input data and functions of DPW (Digital Photogrammetric Workstation). On quantitative aspect, accuracy of each steps were measured by increasing 2 GCPs each time from the minimum of 6 GCPs. In DLT, with the minimum of 10 points, RMSE is 2 pixels at most. Besides that, interpretation and stereoscopic plotting using KOMPSAT-1 imagery and other simulated imagery was performed. The tests resulted that, for KOMPSAT-1 (6.6m) stereoscopic images, the possibility of interpretation is 44.79% and possibility of stereoscopic plotting is 43.75%. In the other hand, for simulated imagery (1m), the possibility of interpretation is 60.92% and possibility of stereoscopic plotting is 55.18%.

• Korean Journal of Radiology
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• v.22 no.8
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• pp.1225-1239
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• 2021

During the past decade, researchers have investigated the use of computer-aided mammography interpretation. With the application of deep learning technology, artificial intelligence (AI)-based algorithms for mammography have shown promising results in the quantitative assessment of parenchymal density, detection and diagnosis of breast cancer, and prediction of breast cancer risk, enabling more precise patient management. AI-based algorithms may also enhance the efficiency of the interpretation workflow by reducing both the workload and interpretation time. However, more in-depth investigation is required to conclusively prove the effectiveness of AI-based algorithms. This review article discusses how AI algorithms can be applied to mammography interpretation as well as the current challenges in its implementation in real-world practice.