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RISS 인기검색어
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- 저자 : ( Joonseo Kang ) (검색결과 3 건)
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Kang Joonseo,Kwon Young-Joon 대한말초신경학회 2023 The Nerve Vol.9 No.2
Objective: Minimally invasive (MI) surgery for the treatment of spinal stenosis is currently a topic of substantial interest. One such technique is the unilateral biportal endoscopic (UBE) method, becoming popular among spine surgeons as a MI alternative to decompressive lumbar laminectomy without fusion. The purpose of this study was to present a description of the surgical technique and early clinical and radiological outcomes following the author's adoption of the UBE surgical technique for decompression of spinal stenosis.Methods: Between 2019 and 2021, surgery was performed on 47 patients with lumbar spinal stenosis. Clinical and radiological data were retrospectively analyzed through electronic medical records and imaging software reviews. Questionnaires and radiologic images were prospectively collected at scheduled times. The surgical technique used two corridors: one for endoscopic viewing and the other for handling surgical instruments during the procedure. Clinical outcomes were measured using the visual analogue scale (VAS) and the Oswestry Disability Index (ODI), while radiological outcomes were evaluated using X-rays to assess instability. Outcomes after UBE surgery were evaluated in terms of changes in clinical and radiological parameters from the baseline. A mixed-effects model with random effects for patients and surgical levels was used to test for differences in repeatedly measured clinical and radiological parameters.Results: During the early postoperative period, there were few complications, and all patients had a smooth recovery. Patients reported minimal postoperative wound discomfort. Back and leg VAS scores improved significantly in the early postoperative period (at 3, 6, and 12 months) compared to the baseline preoperative scores (p<0.001). The ODI also showed significant improvement post-operatively (p<0.001). The X-ray parameters were well maintained and did not show any progression of instability during the follow-up period.Conclusion: UBE surgery is a safe and effective MI technique for treating lumbar stenosis, with good early results and few complications during the early learning curve period.
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Extensive Leptomeningeal Spreading of Ependymoma in an Adult: Case Report and Literature Review
( Joonseo Kang ),( Kwon Woo Lee ),( Yeongu Chung ),( Yusam Won ),( Je Beom Hong ) 대한뇌종양학회·대한신경종양학회·대한소아뇌종양학회 2023 Brain Tumor Research and Treatment Vol.11 No.4
Ependymoma is a rare adult tumor that originates from ependymal cells of the central nervous system, primarily occurring in the cerebral ventricles or the central canal of the spinal cord. In this paper, we report a case of extensive leptomeningeal seeding of ependymoma of a 39-year-old male patient, in whom the tumor was found incidentally after head trauma. The MRI exhibited diffuse leptomeningeal infiltrative lesions along with bilateral multiple cerebral sulci, basal cisterns, cerebellopontine angle, cerebellar folia. It also showed multinodular enhancing T1 low T2 high signal intensity lesions along the whole spinal cord. After the tumor biopsy at right temporal lesion, pathologic diagnosis was classic ependymoma (WHO grade 2). The patient has undergone radiation therapy and chemotherapy, and is currently maintaining a stable condition two years after surgery. This report suggests that when considering the differential diagnosis of extensive lesions both in the intracranial and intraspinal space, ependymoma should also be considered.
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강준서,임창원,Kang, Joonseo,Lim, Changwon 한국통계학회 2021 응용통계연구 Vol.34 No.2
시각질의응답과 이미지 캡셔닝은 이미지의 특징과 문장의 언어적인 특징을 이해하는 것을 요구하는 작업이다. 따라서 두 가지 작업 모두 이미지와 텍스트를 연결해 줄 수 있는 공동 어텐션이 핵심이라고 할 수 있다. 본 논문에서는 MSCOCO 데이터 셋에 대하여 사전 훈련된 transformer 모델을 이용 하여 캡션을 생성한 후 이를 활용해 시각질의응답의 성능을 높이는 모델을 제안하고자 한다. 이때 질 문과 관계없는 캡션은 오히려 시각질의응답에서 답을 맞히는데 방해가 될 수 있기 때문에 질문과의 유사도를 기반으로 질문과 유사한 일부의 캡션을 활용하도록 하였다. 또한 캡션에서 불용어는 답을 맞히는데 영향을 주지 못하거나 방해가 될 수 있기 때문에 제거한 후에 실험을 진행하였다. 기존 시 각질의응답에서 이미지와 텍스트간의 공동 어텐션을 활용하여 좋은 성능을 보였던 deep modular co-attention network (MCAN)과 유사도 기반의 선별된 캡션을 사용하여 VQA-v2 데이터에 대하여 실험을 진행하였다. 그 결과 기존의 MCAN모델과 비교하여 유사도 기반으로 선별된 캡션을 활용했을 때 성능 향상을 확인하였다. Visual Question Answering (VQA) and image captioning are tasks that require understanding of the features of images and linguistic features of text. Therefore, co-attention may be the key to both tasks, which can connect image and text. In this paper, we propose a model to achieve high performance for VQA by image caption generated using a pretrained standard transformer model based on MSCOCO dataset. Captions unrelated to the question can rather interfere with answering, so some captions similar to the question were selected to use based on a similarity to the question. In addition, stopwords in the caption could not affect or interfere with answering, so the experiment was conducted after removing stopwords. Experiments were conducted on VQA-v2 data to compare the proposed model with the deep modular co-attention network (MCAN) model, which showed good performance by using co-attention between images and text. As a result, the proposed model outperformed the MCAN model.
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