Korea University

About: Korea University is a education organization based out in Seoul, South Korea. It is known for research contribution in the topics: Population & Thin film. The organization has 39756 authors who have published 82424 publications receiving 1860927 citations. The organization is also known as: Bosung College & Bosung Professional College.

2012 HRS/EHRA/ECAS expert consensus statement on catheter and surgical ablation of atrial fibrillation: Recommendations for patient selection, procedural techniques, patient management and follow-up, definitions, endpoints, and research trial design

Abstract: During the past decade, catheter ablation of atrial fibrillation (AF) has evolved rapidly from an investigational procedure to its current status as a commonly performed ablation procedure in many major hospitals throughout the world. Surgical ablation of AF, using either standard or minimally invasive techniques, is also performed in many major hospitals throughout the world. In 2007, an initial Consensus Statement on Catheter and Surgical AF Ablation was developed as a joint effort of the Heart Rhythm Society, the European Heart Rhythm Association, and the European Cardiac Arrhythmia Society.1 The 2007 document was also developed in collaboration with the Society of Thoracic Surgeons and the American College of Cardiology. Since the publication of the 2007 document, there has been much learned about AF ablation, and the indications for these procedures have changed. Therefore the purpose of this 2012 Consensus Statement is to provide a state-of-the-art review of the field of catheter and surgical ablation of AF and to report the findings of a Task Force, convened by the Heart Rhythm Society, the European Heart Rhythm Association, and the European Cardiac Arrhythmia Society and charged with defining the indications, techniques, and outcomes of this procedure. Included within this document are recommendations pertinent to the design of clinical trials in the field of AF ablation, including definitions relevant to this topic. This statement summarizes the opinion of the Task Force members based on an extensive literature review as well as their own experience. It is directed to all health care professionals who are involved in the care of patients with AF, particularly those who are undergoing, or are being considered for, catheter or surgical ablation procedures for AF. This statement is not intended to recommend or promote catheter ablation of AF. Rather the ultimate judgment regarding care of a particular patient …

BioBERT: a pre-trained biomedical language representation model for biomedical text mining.

Abstract: Motivation Biomedical text mining is becoming increasingly important as the number of biomedical documents rapidly grows. With the progress in natural language processing (NLP), extracting valuable information from biomedical literature has gained popularity among researchers, and deep learning has boosted the development of effective biomedical text mining models. However, directly applying the advancements in NLP to biomedical text mining often yields unsatisfactory results due to a word distribution shift from general domain corpora to biomedical corpora. In this article, we investigate how the recently introduced pre-trained language model BERT can be adapted for biomedical corpora. Results We introduce BioBERT (Bidirectional Encoder Representations from Transformers for Biomedical Text Mining), which is a domain-specific language representation model pre-trained on large-scale biomedical corpora. With almost the same architecture across tasks, BioBERT largely outperforms BERT and previous state-of-the-art models in a variety of biomedical text mining tasks when pre-trained on biomedical corpora. While BERT obtains performance comparable to that of previous state-of-the-art models, BioBERT significantly outperforms them on the following three representative biomedical text mining tasks: biomedical named entity recognition (0.62% F1 score improvement), biomedical relation extraction (2.80% F1 score improvement) and biomedical question answering (12.24% MRR improvement). Our analysis results show that pre-training BERT on biomedical corpora helps it to understand complex biomedical texts. Availability and implementation We make the pre-trained weights of BioBERT freely available at https://github.com/naver/biobert-pretrained, and the source code for fine-tuning BioBERT available at https://github.com/dmis-lab/biobert.

Deep Learning in Medical Image Analysis

Abstract: This review covers computer-assisted analysis of images in the field of medical imaging. Recent advances in machine learning, especially with regard to deep learning, are helping to identify, classify, and quantify patterns in medical images. At the core of these advances is the ability to exploit hierarchical feature representations learned solely from data, instead of features designed by hand according to domain-specific knowledge. Deep learning is rapidly becoming the state of the art, leading to enhanced performance in various medical applications. We introduce the fundamentals of deep learning methods and review their successes in image registration, detection of anatomical and cellular structures, tissue segmentation, computer-aided disease diagnosis and prognosis, and so on. We conclude by discussing research issues and suggesting future directions for further improvement.