Welcome to MINDS!

Established in 2020, POSTECH Mathematical Institute for Data Science (MINDS) is the community of researchers in the areas of fundamental data science, machine learning, artificial intelligence, scientific computing, and humanitarian data science. MINDS mission is to provide a platform for collaboration among researchers and to provide various opportunities for students in data science. MINDS also aims to use our data science research to serve our local and global communities pursuing humanitarian data science.

News

🌟 DACON Ranker Special Lecture: Winning Strategies for AI Competitions 🌟 2023.11.30

3nd POSTECH&Peking SIAM Student Chapter Joint Conference 2023.11.01

2023 PSSC Summer Camp 2023.10.04

[POSTECH(포항공과대학교) 수리 데이터과학 연구소 연구계약직 공고]-상시모집 2023.07.25

[POSTECH(포항공과대학교) 수리 데이터과학 연구소 연구계약직 공고] 2023.07.14

[POSTECH(포항공과대학교) 수리 데이터과학 연구소 연구교수 채용 공고] 2023.06.12

Seminar | Joint seminar for probability and mathematical biology 2023.05.02

[POSTECH(포항공과대학교) 수리 데이터과학 연구소 연구계약직 공고] 2023.02.15

Upcoming Events

Schedule

MINDS SEMINAR

MINDS Seminar Series | Chul Moon (Southern Methodist University) - Using Persistent Homology Topological Features to Characterize Medical Images: Case Studies on Lung and Brain Cancers

MINDS SEMINAR

period : 2022-11-22 ~ 2022-11-22

time : 10:00:00 ~ 11:00:00

개최 장소 : Online streaming (Zoom)

Topic : Using Persistent Homology Topological Features to Characterize Medical Images: Case Studies on Lung and Brain Cancers

개요

Date	2022-11-22 ~ 2022-11-22	Time	10:00:00 ~ 11:00:00
Speaker	Chul Moon	Affiliation	Southern Methodist University
Place	Online streaming (Zoom)	Streaming link	ID : 688 896 1076 / PW : 54321
Topic	Using Persistent Homology Topological Features to Characterize Medical Images: Case Studies on Lung and Brain Cancers
Contents	Tumor shape is a key factor that affects tumor growth and metastasis. We propose a topological feature computed by persistent homology to characterize tumor progression from digital pathology and radiology images and examine its effect on the time-to-event data. The proposed model enables interpretable inference about the association between topological shape features and survival risks. Two case studies are conducted using 133 lung cancer and 77 brain tumor patients. The results of both studies show that the topological features predict survival prognosis after adjusting clinical variables, and the predicted high-risk groups have worse survival outcomes than the low-risk groups. Also, the topological shape features found to be positively associated with survival hazards are irregular and heterogeneous shape patterns, which are known to be related to tumor progression.