전은지, 정승우, 허다운 연구원이 한국인공지능학회 주관한 2021 하계학술대회에 제출한 논문이 “엘지이노텍 최우수논문상”을 수상하였습니다. 축하합니다!

Congratulations!!! 3 papers have been accepted to CKAIA 2021.

Congratulations!!! Two papers by Jaein and Wonsik are accepted for presentation in MICCAI, 2021.

Congratulation!! A paper on leveraging population-based and personalized reference Intervals for liver and spleen volumes in healthy individuals and those with viral hepatitis has been accepted for publication in Radiology. D.W. Kim, J. Ha, S. Lee, J.H. Kwon, N.Y. Kim, Y. Sung, J.-S. Yoon, H.-I. Suk, Y. Lee, and B.-K. Kang, “Population-based and Personalized Reference Intervals for Liver and Spleen Volumes in healthy individuals and those with viral hepatitis,” Radiology, Accepted.

Congratulations!!! Wisnu’s work of “ProtoBrainMaps: Prototypical Brain Maps for Alzheimer’s Disease Progression Modeling” is accepted for presentation in MIDL.

Congratulation!! Our co-work with Prof. Min on “Electrophysiological Decoding of Spatial and Color Processing in Human Prefrontal Cortex” is accepted for publication in NeuroImage. B.-K. Min*, H. S. Kim, W. Ko, M.-H. Ahn, H.-I. Suk, D. Pantazis, R. T. Knight, “Electrophysiological Decoding of Spatial and Color Processing in Human Prefrontal Cortex,” NeuroImage, Accepted. (* Corresponding author)

Congratulations!!! Wonjun and Eunjin’s review paper, “A Survey on Deep Learning-based Short/Zero-calibration Approaches for EEG-based Brain-Computer Interfaces,” has been accepted for publication in Frontiers in Human Neuroscience (JCR-IF: 2.673).

Congratulations!!! Wonsik’s work on “Deep Recurrent Model for Individualized Prediction of Alzheimer’s Disease Progression” is accepted for publication in NeuroImage.

Congratulations!!! Jiyeon and Wonjun’s paper, titled “A Unified Framework for Personalized Regions Selection and Functional Relation Modeling for Early MCI Identification,” is accepted for publication in NeuroImage.

Functional MRI – and Machine/Deep-Learning-based Brain Disorder/Disease Diagnosis and Prognosis We seek a couple of highly motivated graduate students on functional brain imaging analysis and machine/deep learning-based brain disease diagnosis and prognosis. It is especially encouraged for those eager to develop advanced brain network modeling and functional MRI-based diagnostic methods by developing or applying recent state-of-the-art machine/deep learning algorithms. We welcome all applicants with the background in cognitive neuroscience, neuroscience, biomedical engineering, computer science, applied mathematics/statistics, electrical engineering, signal/image processing and or other related fields. A good programming skill (Python, Matlab) is also highly preferred.  If interested, please email your...