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RISS 인기검색어
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- 저자 : 이태범(Lee TaeBum) (검색결과 3 건)
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화물 검색 시스템을 위한 듀얼 에너지 X-ray 검색기 영상을 이용한 물질 추정 방법
이태범(Lee TaeBum),강현수(Kang HyunSoo) 한국산업정보학회 2018 한국산업정보학회논문지 Vol.23 No.1
본 논문은 듀얼 에너지 X–ray 검색기의 영상을 이용한 물질의 추정 방법 알고리즘을 제안한다. 물질 추정 알고리즘으로 많이 사용되는 기존 4가지 분별 곡선 이외에 로그 함수를 사용한 새로운 분별곡선을 이용하여 물질을 분류한다. 여기에 기존의 선형 보간을 이용한 원자번호 추정방법이 아닌 확률분포를 이용한 원자번호 추정 방법을 제시한다. 확률분포를 이용한 가중치 계산에는 근접한 두 기준물질을 사용하는 방법과 모든 기준물질을 사용하는 방식, 2가지 방식을 실험하였다. 확률분포를 가중치로 사용하여 물질의 원자번호를 추정 할 경우 기존의 방법보다 더 정확한 원자번호 추정 결과를 나타내었다. 추정된 원자번호를 육안으로 확인하기 위하여 HSI 모델을 이용하여 결과영상에 채색하였다. This paper presents a material estimation method using dual-energy X-ray images generated as a result of cargo inspection system in MeV region. We use new discrimination curve using logarithmic function rather than four discrimination curves commonly used in existing estimation algorithms. We also propose an atomic number estimation using the probability distribution of the logarithmic curve rather than linear interpolation. When the probability distribution is used as a weight, we used two methods of using the weight for the two nearest reference materials and the weight for all the reference materials. Experimental results showed that the atomic number estimation of materials using the probability distribution as a weight is more accurate than the existing methods. In order to visualize the estimated atomic number, the HSI model was used for color the resulting image.
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Taebum Lee,이태범,이보람,최윤라,한정호,안명주,엄상원 대한병리학회 2016 Journal of Pathology and Translational Medicine Vol.50 No.3
Background: Although epidermal growth factor receptor (EGFR), v-Ki-ras2 Kirsten rat sarcoma viral oncogene (KRAS), and anaplastic lymphoma kinase (ALK) mutations in non-small cell lung cancer (NSCLC) were thought to be mutually exclusive, some tumors harbor concomitant mutations. Discovering a driver mutation on the basis of morphologic features and therapeutic responses with mutation analysis can be used to understand pathogenesis and predict resistance in targeted therapy. Methods: In 6,637 patients with NSCLC, 12 patients who had concomitant mutations were selected and clinicopathologic features were reviewed. Clinical characteristics included sex, age, smoking history, previous treatment, and targeted therapy with response and disease-free survival. Histologic features included dominant patterns, nuclear and cytoplasmic features. Results: All patients were diagnosed with adenocarcinoma and had an EGFR mutation. Six patients had concomitant KRAS mutations and the other six had ALK mutations. Five of six EGFR-KRAS mutation patients showed papillary and acinar histologic patterns with hobnail cells. Three of six received EGFR tyrosine kinase inhibitor (TKI) and showed partial response for 7–29 months. All six EGFR-ALK mutation patients showed solid or cribriform patterns and three had signet ring cells. Five of six EGFR-ALK mutation patients received EGFR TKI and/or ALK inhibitor and four showed partial response or stable disease, except for one patient who had acquired an EGFR mutation. Conclusions: EGFR and ALK mutations play an important role as driver mutations in double mutated NSCLC, and morphologic analysis can be used to predict treatment response.
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보티트엉비 ( Vi Thi-tuong Vo ),김애라 ( Aera Kim ),이태범 ( Taebum Lee ),김수형 ( Soo-hyung Kim ) 한국정보처리학회 2021 한국정보처리학회 학술대회논문집 Vol.28 No.2
Survival time analysis is one of the main methods used by the pathologist to prognosis for cancer patients. In this paper, we strive to estimate the individual survival time of Adenocarcinoma (ADC) lung cancer patients from pathological images by adopting the convolutional neural network called the SurvPatchV1 model. First, we extracted tissue patches from the whole-slide images (WSI) to deal with extremely large dimensions of WSI. Then the survival time of each patch is estimated through the SurvPatchV1 model. Finally, the individual survival time of each patient is computed. The proposed method is trained and tested on the subset of the NLST dataset for ADC lung cancer. The result demonstrates that our model can obtain all tissue information in lieu of only tumor information in a whole pathological image to estimate the individual survival time.
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