About

Keun-Soo Heo (허근수)

Date	Record
2020 ~	Ph.D. candidate, Department of Artificial Intelligence, Korea University
2014 ~ 2020	B.S., Department of Information, Communication and Electronic Engineering, The Catholic University of Korea

Date	Record
2020 ~	Ph.D. candidate, Medical Artificial Intelligence Lab.(MAILab), Korea University
2017 ~ 2020	Undergraduate researcher, Image Signal Processing Lab., The Catholic University of Korea

Jun-Mo Kim, Keun-Soo Heo, Dong-Hee Shin, Hyeonyeong Nam, Dong-Ok Won, Ji-Hoon Jeong, and Tae-Eui Kam, “A Learnable Continuous Wavelet-based Multi-Branch Attentive Convolutional Neural Network for Spatio-Spectral-Temporal EEG Signal Decoding,” Expert Systems with Applications, 2024. (Accepted)
Sanghyeon Cho, Bogyeong Kang, Keun-Soo Heo, Eunjung Jo, and Tae-Eui Kam,”Enhanced Structure Preservation and Multi-View Approach in Unsupervised Domain Adaptation for Optic Disc and Cup Segmentation”, 2024 IEEE 21st International Symposium on Biomedical Imaging (ISBI) (Accepted).
Minjoo Lim, Keun-Soo Heo, Jun-mo Kim, Bogyeong Kang, Weili Lin, Han Zhang, Dinggang Shen, and Tae-Eui Kam, “A Unified Multi-Modality Fusion Framework for Deep Spatio-Temporal-Spectral Feature Learning in Resting-State fMRI Denoising” IEEE Journal of Biomedical and Health Informatics, 2024. [paper] (Accepted)
Bogyeong Kang, Hyeonyeong Nam, Ji-Wung Han, Keun-Soo Heo, Tae-Eui Kam, “Multi-view Cross-Modality MR Image Translation for Vestibular Schwannoma and Cochlea Segmentation”, Proc, MICCAI BrainLes 2022 workshop, Singapore, Sep. 18-22, 2022. [paper]
Keun-Soo Heo, Dong-Hee Shin, Sheng-Che Hung, Weili Lin, Han Zhang, Dinggang Shen, Tae-Eui Kam, “Deep Attentive Spatio-Temporal Feature Learning for Automatic Resting-State fMRI Denoising,” NeuroImage, 119127, 2022. [paper] [code]

Keun-Soo Heo, Yunju Kim, Changwoo Lee, “Efficient Deep Neural Network for Restoring Image Intensity,” IEIE Transactions on Smart Processing & Computing, Vol.8, No.2, pp. 121-125, 2019. [paper] [code]