Congratulations!!! Wonsik’s work on “EAG-RS: A Novel Explainability-guided ROI Selection Framework for ASD Diagnosis via Inter-regional Relation Learning,” has been accepted for publication in IEEE-TMI.

Congratulations. 3 papers by Eunsong, Eunjin, and Kwanseok are accepted for presentation at JKAIA2023.

2023년 혁신의료기기 산업 발전에 대한 우수 공적자로 선정되어 식품의약품안전처장 표창을 수상하였다. Professor Heung-Il Suk received the “Award for Contribution to the Development of the Innovative Medical Device Industry” from the Ministry of Food and Drug Safety.

Congratulations!!! Junghyo’s work on “Module of Axis-based Nexus Attention for Weakly Supervised Object Localization” has been accepted for publication at Scientific Reports.

Congratulations!!! Seungwoo’s work on “Deep Efficient Continuous Manifold Learning for Time Series Modeling,” has been accepted for publication in IEEE-TPAMI.

Congratulations!!! Do-Yeon’s work on “Spatiotemporal Graph Neural Networks for Predicting Mid-to-Long-Term PM2.5 Concentrations” has been accepted for publication in the Journal of Cleaner Production (JCR-IF: 11.1).

우리 연구실의 박사졸업생, 고원준 박사가 성신여자대학교 조교수로 임용되었습니다. 그의 학문적 성취와 헌신에 박수를 보냅니다. 향후의 성공과 발전을 기대합니다. 🌟

Congratulations!!! Jaein’s work on “Site-Invariant Meta-Modulation Learning for Multi-Site Autism Spectrum Disorders Diagnosis” has been accepted for publication in IEEE-TNNLS.

Congratulations!!! Eunji’s paper “Medical Transformer: Universal Encoder for 3D Brain MRI Analysis” has been accepted for publication in IEEE Transactions on Neural Networks and Learning Systems. E. Jun, S. Jeong, D.-W. Heo, and H.-I. Suk, “Medical Transformer: Universal Brain Encoder for 3D MRI Analysis,” IEEE Transactions on Neural Networks and Learning Systems (TNNLS), Accepted, 2023. (2022-JCR-IF: 10.4, COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE: 14/145, COMPUTER SCIENCE, HARDWARE & ARCHITECTURE: 3/54)

Congratulations!!! Yooseung’s work on “Frequency Mixup Manipulation based Unsupervised Domain Adaptation for Brain Disease Identification” has been accepted for presentation at ACPR2023 (Oral Presentation).