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구개상악재건을 위한 유리피판술에서 다양한 공여부의 선택
윤도원,민희준,김지예,이원재,정섬,정윤규,Yoon, Do-Won,Min, Hee-Jun,Kim, Ji-Ye,Lee, Won-Jae,Chung, Seum,Chung, Yoon-Kyu 대한미세수술학회 2011 Archives of reconstructive microsurgery Vol.20 No.1
Purpose: A palatal defect following maxillectomy can cause multiple problems like the rhinolalia, leakage of foods into the nasal cavity, and hypernasality. Use of a prosthetic is the preferred method for obturating a palate defect, but for rehabilitating palatal function, prosthetics have many shortcomings. In a small defect, local flap is a useful method, however, the size of flap which can be elevated is limited. In 12 cases of palatomaxillary defect, we used various microvascular free flaps in reconstructing the palate and obtained good functional results. Method: Between 1990 and 2004, 12 patients underwent free flap operation after head and neck cancer ablation, and were reviewed retrospectively. Among the 12 free flaps, 6 were latissimus dorsi myocutaneous flaps, 3 rectus abdominis myocutaneous flaps, and 3 radial forearm flaps. Result: All microvascular flap surgery was successful. Mean follow up time was 8 months and after the follow up time all patients reported satisfactory speech and swallowing. Wound dehiscence was observed in 4 cases, ptosis was in 1 case and fistula was in 1 case, however, rhinolalia, leakage of food, or swallowing difficultly was not reported in the 12 cases. Conclusion: We used various microvascular flaps for palatomaxillary reconstruction. For 3-dimensional flap needs, we used the latissimus dorsi myocutaneous flap to obtain enough volume for filling the defect. Two-dimensional flaps were designed with latissimus dorsi myocutaneous flap, rectus abdominis flap and radial forearm flap. For cases with palatal defect only, we used the radial forearm flap. In palatomaxillary reconstruction, we can choose various free flap techniques according to the number of skin paddles and flap volume needed.
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심재원(Jae-Won Shim),남정찬(Jeong-Chan Nam),민희준(Hee-Jun Min),김가연(Ga-Yeon Kim),이효진(Hyo-Jin Lee),정혜영(Hye-Young Jeong) 한국지능시스템학회 2021 한국지능시스템학회논문지 Vol.31 No.4
딥러닝 모델의 성능을 평가하는 지표는 정확도를 평가할 뿐, 해당 모델의 불확실성을 평가할 수 없다. 불확실성은 정확도와는 별개로, 안정성 또는 신뢰도의 의미를 가질 수 있다. 불확실성을 측정할 수 있다면 기존 모델의 성능을 평가하는 지표 외에 새로운 지표가 될 가능성을 가지고 있다. 불확성을 측정하기 위한 딥러닝 모델로 베이지안 신경망을 사용한다. 본 논문은 불확실성이 딥러닝 모델에서 성능을 평가하는 지표가 될 수 있음을 확인해보려 하였다. 불확실성을 측정하기 위한 베이지안 신경망 모델로 베이지안 LSTM (Bayesian Long Short Term Memory)을 사용하였고, 모델의 학습에 실제 데이터를 적용하여 불확실성을 측정하였으며, 이를 기존 모델의 성능 지표와 비교분석 하였다. 실제 데이터를 이용한 실험에서 이상치가 추가됨에 따라 기존 모델의 성능지표는 증가하거나 감소하는 여러 가지 양상을 보이는 반면, 불확실성은 이상치에 따라 일관적으로 증가하는 결과를 보였다. Uncertainty is a separate measure of accuracy, and may have implications for stability or reliability. If uncertainty can be regulated, it has the potential to be a new metric for evaluating a performance of Deep learning models. Deep learning models for measuring uncertainty are Bayesian neural networks, which can be implemented by applying a drop-out scheme to each layer of existing deep learning models. This paper confirms that uncertainty can be an indicator of performance evaluation in deep learning models. We used Bayesian LSTM as a Bayesian neural network model to measure uncertainty, and measured uncertainty, which is compared to performance metrics from existing models.
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