• 제목/요약/키워드: ChestX-ray14

검색결과 133건 처리시간 0.027초

흉부 X-선 영상을 이용한 14 가지 흉부 질환 분류를 위한 Ensemble Knowledge Distillation (Ensemble Knowledge Distillation for Classification of 14 Thorax Diseases using Chest X-ray Images)

  • 호티키우칸;전영훈;곽정환
    • 한국컴퓨터정보학회:학술대회논문집
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    • 한국컴퓨터정보학회 2021년도 제64차 하계학술대회논문집 29권2호
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    • pp.313-315
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    • 2021
  • Timely and accurate diagnosis of lung diseases using Chest X-ray images has been gained much attention from the computer vision and medical imaging communities. Although previous studies have presented the capability of deep convolutional neural networks by achieving competitive binary classification results, their models were seemingly unreliable to effectively distinguish multiple disease groups using a large number of x-ray images. In this paper, we aim to build an advanced approach, so-called Ensemble Knowledge Distillation (EKD), to significantly boost the classification accuracies, compared to traditional KD methods by distilling knowledge from a cumbersome teacher model into an ensemble of lightweight student models with parallel branches trained with ground truth labels. Therefore, learning features at different branches of the student models could enable the network to learn diverse patterns and improve the qualify of final predictions through an ensemble learning solution. Although we observed that experiments on the well-established ChestX-ray14 dataset showed the classification improvements of traditional KD compared to the base transfer learning approach, the EKD performance would be expected to potentially enhance classification accuracy and model generalization, especially in situations of the imbalanced dataset and the interdependency of 14 weakly annotated thorax diseases.

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컨볼루션 뉴럴 네트워크 기반의 딥러닝을 이용한 흉부 X-ray 영상의 분류 및 정확도 평가 (Evaluation of Classification and Accuracy in Chest X-ray Images using Deep Learning with Convolution Neural Network)

  • 송호준;이은별;조흥준;박세영;김소영;김현정;홍주완
    • 한국방사선학회논문지
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    • 제14권1호
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    • pp.39-44
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    • 2020
  • 본 연구에서는 CNN과 빅데이터 기술을 이용한 Deep Learning을 통해 흉부 X-ray 영상 분류 및 정확성 연구에 대하여 알아보고자 한다. 총 5,873장의 흉부 X-ray 영상에서 Normal 1,583장, Pneumonia 4,289장을 사용하였다. 데이터 분류는 train(88.8%), validation(0.2%), test(11%)로 분류하였다. Convolution Layer, Max pooling layer pool size 2×2, Flatten layer, Image Data Generator로 구성하였다. Convolution layer가 3일 때와 4일 때 각각 filter 수, filter size, drop out, epoch, batch size, 손실함수 값을 설정하였다. test 데이터로 Convolution layer가 4일 때, filter 수 64-128-128-128, filter size 3×3, drop out 0.25, epoch 5, batch size 15, 손실함수 RMSprop으로 설정 시 정확도가 94.67%였다. 본 연구를 통해 높은 정확성으로 분류가 가능하였으며, 흉부 X-ray 영상뿐만 아니라 다른 의료영상에서도 많은 도움이 될 것으로 사료된다.

빠르게 성장한 거대 종격동 양성기형종 (Rapidly Grown Huge Mediastinal Benign Teratoma ; one case report)

  • 조성우;지현근;안현성;신윤철;남은숙
    • Journal of Chest Surgery
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    • 제33권6호
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    • pp.521-524
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    • 2000
  • The benign teratoma is usually slow growing tumor, but we expirienced a case of primary huge mediastinal benign teratoma that had grown very rapidly, maximally during 3 years. The 14-year-old female patient was admitted to our hospital because of abnormal chest X-ray that showed 10$\times$10cm sized well definded mass with multiple calcificactions. but the mass was not present in chest X-ray perfomed on 3 years prior to admission. Under the diagnosis of teratoma, complete surgical resection was done by the left thoracotomy. The result of pathology was benign teratoma.

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Inception V3를 이용한 흉부촬영 X선 영상의 폐렴 진단 분류 (Diagnostic Classification of Chest X-ray Pneumonia using Inception V3 Modeling)

  • 김지율;예수영
    • 한국방사선학회논문지
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    • 제14권6호
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    • pp.773-780
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    • 2020
  • 4차 산업의 발전으로 의학·보건·바이오 등 여러 과학기술 분야에서는 질병을 예방하고 질병에 대한 피해를 줄이기 위한 연구가 이루어지고 있으며, 최근에는 ICT 기술의 발전과 더불어 인공지능 기술이 급부상하고 그 효용성이 입증되면서 영상의학 검사의 영상 분석에 인공지능 기술이 도입되어 연구되고 있다. 본 논문에서는 흉부 X선 영상을 이용하여 폐렴의 분류와 검출에 대한 딥러닝 모델을 직접 적용해보고 실제로 Inception 계열의 딥러닝 모델이 폐렴 검출에 있어 유용한 모델인지 평가하고자 한다. 실험재료는 캐글(Kaggle)에서 무료로 제공 및 공유하는 흉부 X선 영상 데이터 세트를 사용하였으며 전체 3,470개의 흉부 X선 영상 데이터 중 학습 데이터 세트 1,870개, 검증 데이터 세트 1,100개, 테스트 데이터 세트 500개로 분류하였다. 실험결과 Inception V3 딥러닝 모델의 Metric 평가에 대한 결과값은 정확도는 94.80%, 정밀도는 97.24%, 재현율은 94.00%, F1 스코어는 95.59의 결과값을 나타내었다. 그리고 흉부 X선 영상의 페렴 검출 및 분류에 대하여 Inception V3 딥러닝 모델링에 대한 최종 에포크의 정확도는 학습 모델링의 경우 94.91%, 검증 모델링은 89.68%의 정확도를 나타내었다. 손실함수 값의 평가는 학습 모델링은 1.127%, 검증 모델링은 4.603%의 손실함수 값을 나타내었다. 이러한 결과로 Inception V3 딥러닝 모델은 흉부영상 데이터의 특징 추출 및 분류에 있어 매우 우수한 딥러닝 모델이며 학습상태 또한 매우 우수하다고 평가하였다. 테스트 모델링에 대한 매트릭스 정확도 평가 결과 정상 흉부 X선 영상 데이터의 경우 96%, 폐렴 흉부 X선 영상데이터의 경우 97%의 정확도가 입증되었다. Inception 계열의 딥러닝 모델의 경우 흉부 질환의 분류에 있어 유용한 딥러닝 모델이 될 것이라고 판단되며 인력의 보조적인 역할 또한 수행할 수 있을 것이라고 기대되어 부족한 의료인력 문제에도 해결점이 될 것이라고 사료된다. 향후 딥러닝을 이용한 폐렴의 진단에 대한 유사 연구 시 본 연구는 유사 연구의 기초자료로 제시될 것이라고 기대된다.

종격동 림프관종 - 1예 보고 - (Mediastinal Lymphangioma - A case report -)

  • 김대현;김수철;조규석
    • Journal of Chest Surgery
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    • 제40권5호
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    • pp.392-394
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    • 2007
  • 14세 남자가 흉부 X-선 검사에서 이상이 발견되어 입원하였다. 흉부 컴퓨터단층촬영에서 전방부 종격동 및 비장에 $14\times14cm$$2\times2cm$크기의 낭성 병변이 보였다. 정중 흉골절개술을 통해 종격동의 병변을 완전 절제하였고, 최종 조직학적 진단은 낭성 림프관종이었다. 현재 수술 후 14개월째로 종격동에서 재발은 없고, 비장의 병변은 경과를 관찰 중이다.

흉부 방사선영상의 좌, 우 반전 발생 여부 컨벌루션 신경망 기반 정확도 평가 (An Accuracy Evaluation on Convolutional Neural Network Assessment of Orientation Reversal of Chest X-ray Image)

  • 이현우;오주영;이주영;이태수;박훈희
    • 대한방사선기술학회지:방사선기술과학
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    • 제43권2호
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    • pp.65-70
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    • 2020
  • PA(postero-anterior) and AP(antero-posterior) chest projections are the most sought-after types of all kinds of projections. But if a radiological technologist puts wrong information about the position in the computer, the orientation of left and right side of an image would be reversed. In order to solve this problem, we utilized CNN(convolutional neural network) which has recently utilized a lot for studies of medical imaging technology and rule-based system. 70% of 111,622 chest images were used for training, 20% of them were used for testing and 10% of them were used for validation set in the CNN experiment. The same amount of images which were used for testing in the CNN experiment were used in rule-based system. Python 3.7 version and Tensorflow r1.14 were utilized for data environment. As a result, rule-based system had 66% accuracy on evaluating whether the orientation reversal on chest x-ray image. But the CNN had 97.9% accuracy on that. Being overcome limitations by CNN which had been shown on rule-based system and shown the high accuracy can be considered as a meaningful result. If some problems which can occur for tasks of the radiological technologist can be separated by utilizing CNN, It can contribute a lot to optimize workflow.

자동현상 지능화 보충방식의 임상적응에 관한 연구 (A Study on the Clinical Application of Intelligent Replenishment System of Automatic X-ray Film Processor Based on Film Density)

  • 이원홍;서상신;인경환;이형진;김건중;윤종현;오용호
    • 대한방사선기술학회지:방사선기술과학
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    • 제22권1호
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    • pp.49-53
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    • 1999
  • To inquire its usefulness of the clinical application of intelligent replenishment system of automatic X-ray film processor based on film density, we processed the serial 300 sheets of radiographic film of chest [$14{\times}14"$, HR-C type] and bone [elbow & ankle($8{\times}10"$), skull($10{\times}12"$), hand & foot($11{\times}14"$), pelvis($14{\times}17"$), HR-G type, 68, 70, 77, 85 sheets respectively]. We analyzed the characteristic corves, relative speeds, average gradients and base plus fog densities every twenty five sheets. We also evaluated the developer and fixer replenishment volumes every that time. In the chest and bone radiograph two all, the characteristic curves were little change, and the relative speeds, average gradients and base plus fog densities were within the maximum control limits. The average developer replenishment volumes were about 43m1/sheet and 39m1/sheet respectively. It brings decreased results about 29% in comparison with the conventional replenishment system. In our experiences, we conclude that the intelligent replenishment system of automatic X-ray film processor based on film density maintains image quality consistently, decreases also the replenishment volumes. Therefore, this system will be resulted in economic and environmental effects, and solve problems of over and low replenishment volume.

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부산지역 의료기관의 흉부촬영 조건과 피폭선량에 관한 조사연구 (A Study on Radiographical Conditions and Exposure Doses During Chest Radiography at Medical Facilities in Pusan)

  • 전성오;조영하
    • 대한방사선기술학회지:방사선기술과학
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    • 제20권2호
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    • pp.49-55
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    • 1997
  • This study was carried out to investigate radiographical and operating conditions of X-ray units and exposure doses to patients during chest radiography, so that the results could provide basic data used for reducing the exposure dose and for providing the diagnostic information with better quality. The conditions and exposure doses of 100 X-ray units mainly used for chest radiography were examined and also 100 radiological technologists mainly handling those apparatus at 76 medical facilities in Pusan were surveyed using a questionnaire from October 1 to December 31 in 1995. The following results were obtained from the study : 1. It was found that most units were capable of taking a high tube voltage radiography by showing 67% of the units equipped with the maximum tube voltage of 150 kV, 94% with more than 500 mA for the rating capacity and 85% with the full wave type of a signal phase. 2. For actual chest radiographical conditions, however, 80% of the units were operated at $60{\sim}100\;kVp$ and only 14% at 100 kVp and over for the high tube voltage. 3. The average exposure time was less than 0.1 second, and eighty four percent of the units adapted the X-ray tube currents ranging from 200 to 300 mA, 80% the focus-film distances between 180 and 210 cm, and 63% the focus sizes of more than 2.0 mm. 4. Most units(98%) employed additional filters made of aluminum, 75% the thickness of filters less than 2.0 mm, and only 2 units the compound filters. 5. Ortho chromatic system was only adopted in 13% of screen film system for the units, and 73% used the grid ratio at 8 : 1 for the low tube voltage during chest radiography. 6. The average exposure dose of all X-ray units during chest radiography was $371\;{\mu}Sv$ with a difference of about 16 times between the minimum to the maximum, and $386\;{\mu}Sv$ both at hospitals and at health centers, followed by $380\;{\mu}Sv$ at general hospitals and $263\;{\mu}Sv$ at university hospitals without showing any statistically significant differences. In conclusion, since patients during chest radiography at medical facilities in Pusan exposed to high levels of radiation, it is recommended that appropriate added filters and grids necessary for the high tube voltage radiography and high-speed screen systems should be adopted and used as soon as possible in order to reduce exposure dose to the patients.

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24년생 장생도라지 약침액(藥鍼液)의 폐렴 증상 개선효과에 대한 임상례 (Clinical report on the improvement of the symptoms of pneumonia by the aqueous extract of Platycodon grandiflorum)

  • 김숙경;최성권;임희정;문익렬;박형선;오수진
    • 대한약침학회지
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    • 제4권3호
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    • pp.59-67
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    • 2001
  • Objective: The purpose of this report is to prove the clinical effect of Platycodon grandiflorum aqueous extract on pneumoniae patients. Methods: We used the aqueous extract of Platycodon grandiflorum to treat two pneumoniae patients. It was injected into five acupuncture points, which was Chondol(天突:CV22) 1 point, Pyesu(肺兪 : BL13) 2 point, and Kworumsu(厥陰兪: BL14) 2 point. Results & conclusions: We have used the aqueous extract of 24-year-old JK for treating the patients suffering from lung diseases, and have experienced the actual effects. Of the treated, two pneumonia-involved patients showed apparent improvement in simple chest X-ray and clinical symptoms. The patients were treated with JK (Jang-saeng platycodon) aqueous extract 25 and 22 times individually. The results were as follows. 1. The symptoms including coughing, phlegm, and fever were improved in two cases. 2. The lung infiltration in simple chest X-ray decreased and the WBC count was kept within normal range in two cases. 3. Side effect such as itching was not found in the process of JK aqueous extract treatment. 4. The criteria for pneumonia are fever, coughing with purulent phlegm, pleural chest pain, the evidence of new infiltration in simple chest X-ray, sign of lung sclerosis in auscultation, increase of WBC count, etc. But they may not be the proper objective diagnostic standards. So we had trouble in statistic process and numerical interpretation. Putting these results together, the JK aqueous extract is considered to be effective in treating patients for pneumonia, and the continuous research and accumulation of data is needed.

흉막에 발생한 국소성 섬유성 종양;1례 보고 (Localized Fibrous Tumor of Pleura; A report of a case)

  • 김남혁
    • Journal of Chest Surgery
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    • 제26권12호
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    • pp.959-961
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    • 1993
  • Localized fibrous tumor of pleura is submesothelial origin and related terms with localized mesothelioma, giant sarcoma of visceral pleura, post-inflammatory tumor of the pleura, pleural fibroma, submesothelial fibroma. This tumor is rare. We experienced a case of localized fibrous tumor.This 66 years old female was admitted with 2 years left persistant flank pain and mild dyspnea. Chest X-ray and CT scan showed a 12x10cm well-defined huge mass in the left subpulmonic area, and not metastatic lesion of any organs.Exploratory thoracotomy was done and a 14x10x8cm [650gm weight] sized mass was excised.The patient was discharged without any complications postoperatively.

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