A Case of Eccrine Adenocarcinoma Misdiagnosed as Epidermal Cyst

Sweat gland cancer is very rare with a reported incidence of less than 0.005% of all tumor specimens resected surgically. Diagnosis and management of these cancers are difficult, due to the limited reports in the literature. Here we present a case of an eccrine adenocarcinoma in the retroauricular area and report this case with a review of the literature.ope

Retention Esophagitis as a Significant Clinical Predictor of Progression to Esophageal Cancer in Achalasia

BACKGROUND/AIMS: Chronic liquid and/or food stasis caused by retention esophagitis (RE) in achalasia is a notable endoscopic finding because of the presence of a thickened or whitish esophageal mucosa and histologically altered squamous hyperplasia. We aimed to identify the clinical features of RE associated with achalasia and to clarify the clinical definition of RE in achalasia as a precancerous lesion identified by analyzing biomarker expressions. METHODS: From 2006 to 2015, we retrospectively reviewed 37 patients with achalasia without previous treatment. Among them, 21 patients had diagnostic findings of RE (RE+) and 16 patients had no diagnostic findings of RE (RE-). Immunohistochemical staining of p53, p16, and Ki-67 was performed on the endoscopic biopsy tissues from the patients with achalasia and 10 control patients with non-obstructive dysphagia. RESULTS: The symptom duration and transit delay were significantly longer in the RE+ group than in the RE- group. We found particularly high p53 positivity rates in the RE+ group (p<0.001). The rate of p16 expression was also significantly higher in the RE+ group than in the other two groups (p=0.003). CONCLUSIONS: A high p53 expression rate was more frequently found in the RE+ group than in the other two groups. RE could be a meaningful clinical feature of achalasia for predicting esophageal carcinogenesis.ope

(A) rule allocation algorithm for efficient distributed complex event processing

학위논문(석사) - 한국과학기술원 : 지식서비스공학대학원, 2019.8,[iv, 38 p. :]복합 이벤트 처리 시스템은 여러 스트림 데이터를 실시간으로 받아 유의미한 복잡한 상황을 이벤트 규칙이나 질의, 연산으로 찾아내고 분석하는 시스템을 말한다. 실시간으로 들어오는 데이터양이 증가하게 되면 이 시스템을 분산시켜 여러 스트림 데이터와 이벤트 규칙을 여러 대의 서버에 분배해 처리하게 된다. 하지만 각 서버에 가해질 부하에 대한 고려 없이 스트림 데이터와 이벤트 규칙을 분배하게 되면 스트림 데이터가 과도하게 복제되어 네트워크 입출력에 부하를 주고 분배된 이벤트 규칙과 스트림 데이터를 맞춰보는 시간이 증가하게 된다. 본 논문에서는 이를 막기 위해 효율적인 스트림 데이터 및 이벤트 규칙 분배 알고리즘을 제안한다. 이 알고리즘은 이벤트 규칙에 점수를 부여하고 점수가 큰 순서대로 이벤트 규칙을 정렬한다. 분배 시에는 주어진 전체 부하 함숫값을 가장 작게 증가시키는 서버에 각 이벤트 규칙을 정렬된 순서대로 하나씩 분배한다. 제안한 알고리즘은 최적화 검증과 성능 실험을 통해 그 우수성을 보였다. 최적화 검증에서는 합성 데이터를 이용해 이 알고리즘이 다른 알고리즘에 비해 최적의 분배 결과에 가장 가깝다는 것을 보인다. 성능 실험에서는 실제 데이터와 이벤트 규칙을 사용한 분산 복합 이벤트 처리 시스템에서 복제율과 지연시간을 다른 대안 알고리즘과 비교해 제안한 알고리즘의 성능 우위를 입증했다. 이후 제안한 알고리즘을 이용한 자동 분산 복합 이벤트 처리 배포 시스템을 개발하고 이를 시연했다.한국과학기술원 :지식서비스공학대학원

시계열 데이터 부족을 위한 레이블 부족 완화

학위논문(박사) - 한국과학기술원 : 데이터사이언스대학원, 2024.2,[vi, 74 p. :]A time series is a sequential set of data points, collected from various sources such as sensor, mobility, and finance. It takes a large cost to annotate every timestamp in a time series because of length and complexity, making hard to recognize patterns in a time series. Label sparsity in time-series data is regarded as a hurdle for its broad applicability, especially in deep learning where huge amount of labels are required. To overcome label sparsity, this dissertation research aims to suggest improve efficiency of few labels in a time series for time series analysis such as classification. The first chapter introduces an active learning algorithm called as TCLP using temporal coherence. Active learning trains an initial model and then queries informative labels to human annotators for re-training the model with the additional labels. As a time series is temporally coherent and the same class lasts for a duration, TCLP propagates the annotated instantaneous label for timestamps in the duration. Propagated labels accelerate model re-training so the model converges faster than before. TCLP estimates the duration of temporal coherence for each newly annotated label and accurately propagate given labels. The second chapter suggests CrossMatch, a method of semi-supervised learning when there is no additional labels but only initial labels. CrossMatch is a consistency regularization framework that trains a model with unlabeled data points by minimizing the difference between the output of a data point and the output of its augmentation. CrossMatch suggests a novel data augmentation method called as context-additive augmentation, which exploits the surrounding contexts of a given sampled instance from a time series. As the length of surrouding contexts can be varied, multiple instances can be augmented and the original instance does not perturbed. Using this property, CrossMatch conducts consistency regularization in more stable manner along. Also reliability-weighted mixing in CrossMatch generates more accurate pseudo-labels that become the target of each augmented instance. The third chapter proposes a change point detection algorithm called as RECURVE that finds class change when there is no available label for further analysis. A recent change point detection algorithm leverages a representation model that outputs a representation at each timestamp. It detects change points by measuring the distance between two representations at consecutive timestamps. However, RECURVE computes curvature of representation trajectory, focusing on more sequential aspect of representations. By using curvature, class change can be detected where neighboring timestamps has similar representation due to temporal coherence. The effectiveness of curvature is proven theoretically using random walk theory and empirically verified by extensive experiments using real datasets. This dissertation is expected to pave a way to employ sparse labels as much as possible and mitigates cost burden for annotating every timestamp in a time series for efficient time-series analysis.한국과학기술원 :데이터사이언스대학원