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Ki-Sun Lee [CV-ENG]

Dentist & Programmer ( Specilized Mediacl AI & Deep Learning)

Tech Stacks

Work Experience

  • 2018-03 ~ Current Clinical Assistant Professor; Korea University Ansan Hospital, An-san, Gyungi do, South Korea
  • 2013-03 ~ 2018-02 Resident & Intern; Korea University Guro Hospital, Guro gu, Seoul, South Korea
  • 2001-07 ~ 2009-02 Programmer; Samsung SDS, Seoul, South Korea
  • 1996-03 ~ 1998-06 Lieutenant; ROTC office Republic of Korea Army, Gwang ju, South Korea

Education

  • 2017-09 ~ 2020-08 Ph.D - Biomedical Engineering, Seoul National University, Seoul, South Korea
  • 2009-05 ~ 2013-02 DDS(Doctor of Dental Surgery) & MDS(Master of Dental Surgery), Chosun University, Gwang ju, South Korea
  • 1992-03 ~ 1996-02 Bachelor of Engineering, Korea University, Seoul, South Korea

Research Project

  • 2022-2025 / National Research Foundation / A deep learning-based dental medical twin system research
  • 2019~2022 / National Research Foundation / Development of osteoporosis screening technology for panoramic images based on deep learning
  • 2020~2021 / Korea University Medical Center / Development of customized deep learning-based clinical decision support system (CDSS) algorithm
  • 2019~2020 / Korea University Medical Center / Optimal design based on finite element analysis for 3D printing implants and prostheses

Research Journals

  • Lee, K.-S.; Lee, E.; Choi, B.; Pyun, S.-B. Automatic Pharyngeal Phase Recognition in Untrimmed Videofluoroscopic Swallowing Study Using Transfer Learning with Deep Convolutional Neural Networks. Diagnostics 2021, 11, 300. https://doi.org/10.3390/diagnostics11020300
  • Lee, K.-S.; Kim, J.Y.; Jeon, E.-T.; Choi, W.S.; Kim, N.H.; Lee, K.Y. Evaluation of Scalability and Degree of Fine-Tuning of Deep Convolutional Neural Networks for COVID-19 Screening on Chest X-ray Images Using Explainable Deep-Learning Algorithm. J. Pers. Med. 2020, 10, 213. https://doi.org/10.3390/jpm10040213
  • Lee, K.-S.; Jung, S.-K.; Ryu, J.-J.; Shin, S.-W.; Choi, J.J.J.o.C.M. Evaluation of Transfer Learning with Deep Convolutional Neural Networks for Screening Osteoporosis in Dental Panoramic Radiographs. 2020, 9, 392. https://www.mdpi.com/2077-0383/9/2/392
  • Lee, K.-S.; Ryu, J.-J.; Jang, H.S.; Lee, D.-Y.; Jung, S.-K.J.A.S. Deep Convolutional Neural Networks Based Analysis of Cephalometric Radiographs for Differential Diagnosis of Orthognathic Surgery Indications. 2020, 10, 2124. https://doi.org/10.3390/app10062124
  • Lee, K.-S.; Shin, J.-H.; Kim, J.-E.; Kim, J.-H.; Lee, W.-C.; Shin, S.-W.; Lee, J.-Y.J.B.r.i. Biomechanical evaluation of a tooth restored with high performance polymer PEKK post-core system: A 3D finite element analysis. 2017, 2017.
  • Lee, K.-S.; Shin, S.-W.; Lee, S.-P.; Kim, J.-E.; Kim, J.-H.; Lee, J.-Y.; Lee, K.-S.; Shin, S.-W.; Lee, S.-P.; Kim, J.-E.J.I.J.o.P. Comparative Evaluation of a Four-Implant-Supported Polyetherketoneketone Framework Prosthesis: A Three-Dimensional Finite Element Analysis Based on Cone Beam Computed Tomography and Computer-Aided Design. 2017, 30.

Awards and Honors

  • 2021 Samsung Medical Center hosted Medical Artificial Intelligence Development Contest, 2nd place in teeth identification in panoramic x-ray
  • 2019 Seoul Asan Hospital hosted Medical Artificial Intelligence Development Contest, 2nd place in breast cancer analysis
  • 2019 Samsung Medical Center hosted Digital Health Hackathon Encouragement Award
  • 2019 NAVER Corp. hosted Open Source Contest Encouragement Award
  • 2018 Korean Associate of Digital Dentistry (KADD) hosted Regular Academic Conference Poster 1st Place
  • 2016 Korea University Medical Center Best Resident Award

Lecture/Presentation/Seminar

Personal Characteristics

  • Multitalented dentist with experience in Artificial Intelligent software development field.
  • Demonstrated excellent skills in Python and Google Deep Learning platform (Tensorflow)
  • Conducting various research projects in the medical field
  • Published a number of research papers related to medical artificial intelligence.
  • True team player with strengths in adaptability and accuracy.

이기선 [CV-KOR-Summary]

코딩하는 치과의사 (의료분야 인공지능 및 딥러닝)

Work Experience (경력)

  • 2018-03 ~ Current 고려대학교 안산병원/임상조교수겸 연구교수
  • 2013-03 ~ 2018-02 고려대학교 구로병원/인턴-레지던트(치과 전문의)
  • 2001-07 ~ 2009-02 삼성SDS 소프트웨어 엔지니어
  • 1996-03 ~ 1998-06 ROTC 장교 복무

Education (학력)

  • 2017-09 ~ 2020-08 서울대학교 의과대학 의공학박사
  • 2009-05 ~ 2013-02 조선대학교 치의학전문대학원 치의학전문석사
  • 1992-03 ~ 1996-02 고려대학교 공학박사

Research Project (연구프로젝트)

  • 2022~2025 / 한국연구재단 / 딥러닝 기반 치과용 메디컬 트윈시스템 개발
  • 2019~2022 / 한국연구재단 / 딥러닝 기반 치과용 파노라마 엑스레이를 이용한 골다공증 스크리닝 기술 개발
  • 2020~2021 / 고려대학교의료원 / 딥러닝 기반 맞춤형 임상의사결정지원시스템(CDSS) 기본 알고리즘 개발
  • 2019~2020 / 고려대학교의료원 / 3D 프린팅 임플란트 및 보철물 유한요소해석 기반 최적설계

Awards and Honors (수상경력)

  • 2021년 삼성서울병원 의료인공지능 개발 경진대회 개최, 파노라마 엑스레이 치아식별 부문 2위
  • 2019 서울아산병원 의료인공지능 개발 경진대회 유방암 분석 2위
  • 2019년 삼성서울병원 디지털헬스 해커톤 장려상
  • 2019 네이버(주) 오픈소스 공모전 장려상 개최
  • 2018년 한국디지털치과학회(KADD) 주최 정기학술대회 포스터 1위
  • 2016 고려대학교의료원 최우수 전공의 표창

저서

Updating...

강의/발표/세미나

언론홍보

교육 및 컨설팅 지원

  • 인공지능 관련 교육 및 컨설팅
  • 딥러닝 및 머신러닝을 통한 문제 해결 관련 교육 및 컨설팅
  • 딥러닝 및 머신러닝 모델 설계 및 제작과정 교육 및 컨설팅

ki-sun Lee's Projects

labelbox icon labelbox

Labelbox is the fastest way to annotate data to build and ship computer vision applications.

lungs-finder icon lungs-finder

Library for detecting lungs on chest x-ray images for further processing. It is fast and able to work on embedded devices.

mediapipe icon mediapipe

Cross-platform, customizable ML solutions for live and streaming media.

medicaldetectiontoolkit icon medicaldetectiontoolkit

The Medical Detection Toolkit contains 2D + 3D implementations of prevalent object detectors such as Mask R-CNN, Retina Net, Retina U-Net, as well as a training and inference framework focused on dealing with medical images.

models icon models

Models and examples built with TensorFlow

mortality-prediction-using-machine-learning-techniques icon mortality-prediction-using-machine-learning-techniques

The prediction of mortality of a human is a foremost challenging task in today’s era. We are evaluating the prediction model on 79999 patients with 342 features. Here we have predicted mortality of patient i.e. (DEAD or ALIVE) who is admitted in the hospital using Deep Neural Networks and various Machine Learning methods where Linear SVM showed the best accuracy.

pixellib icon pixellib

Visit PixelLib's official documentation https://pixellib.readthedocs.io/en/latest/

pyvital icon pyvital

Open source python implementation of medical algorithms

somber icon somber

Recursive Self-Organizing Map/Neural Gas.

sota-medseg icon sota-medseg

SOTA medical image segmentation methods based on various challenges

tensorflow-hub icon tensorflow-hub

A library for transfer learning by reusing parts of TensorFlow models.

tf-speech-recognition-challenge-solution icon tf-speech-recognition-challenge-solution

Source code of the model used in Tensorflow Speech Recognition Challenge (https://www.kaggle.com/c/tensorflow-speech-recognition-challenge). The solution ranked in top 5% in private leaderboard.

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