Hyungseob Shin

Ph.D. candidate at Medical Artificial Intelligence (MAI) Lab., Yonsei University.

adobe_copy2.jpeg

Hi, I am a Ph.D. candidate at MAI-Lab., School of Electrical and Electronic Engineering, Yonsei University. My research interests include computer vision and medical artificial intelligence, particularly:

  • Inverse Problems / Image Reconstruction
  • Unsupervised Domain Adaptation / Cross-Modality Segmentation
  • Computer-Aided Diagnosis / Deep Learning-based Medical Image Classification, Segmentation, and Interpretation

but not limited to.
My expected graduation date is February, 2024.
For inquiries, please contact :mailbox: whatzupsup@yonsei.ac.kr
:link: Links to my Google Scholar and LinkedIn are provided at the bottom.

News

Mar 7, 2023 :page_facing_up: A paper titled “Deep Learning Referral Suggestion and Tumour Discrimination using Explainable Artificial Intelligence applied to Multiparametric MRI” was accepted to European Radiology (JCR 2021 IF=7.034), In Press.
Feb 28, 2023 :page_facing_up: A paper titled “SDC-UDA: Volumetric Unsupervised Domain Adaptation Framework for Slice-Direction Continuous Cross-Modality Medical Image Segmentation” was accepted to CVPR 2023, Vancouver, Canada.
Sep 27, 2021 :tada: Our team (Team Samoyed) won the 1st place in an international challenge for Cross-Modality Domain Adaptation for Medical Image Segmentation (crossMoDA challenge), which was held in conjunction with MICCAI 2021 at Strasbourg, France. :movie_camera: Event Recording. & Presentation.

Selected publications

  1. ER
    Deep Learning Referral Suggestion and Tumour Discrimination using Explainable Artificial Intelligence applied to Multiparametric MRI
    Hyungseob Shin*, Ji Eun Park*, Yohan Jun, and 9 more authors
    European Radiology (In press), 2023
    (JCR2021 IF=7.034)
  2. ER
    Intelligent Noninvasive Meningioma Grading with a Fully Automatic Segmentation using Interpretable Multiparametric Deep Learning
    Yohan Jun*, Yae Won Park*, Hyungseob Shin*, and 7 more authors
    European Radiology, 2023
    (JCR2021 IF=7.034)
  3. CVPR
    SDC-UDA: Volumetric Unsupervised Domain Adaptation Framework for Slice-Direction Continuous Cross-Modality Medical Image Segmentation
    Hyungseob Shin*, Hyeongyu Kim*, Sewon Kim, and 3 more authors
    IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023
    (Acceptance rate ≈ 25.78% (2360/9155))
  4. CVPR
    Joint Deep Model-based MR Image and Coil Sensitivity Reconstruction Network (Joint-ICNet) for Fast MRI
    Yohan Jun, Hyungseob Shin, Taejoon Eo, and 1 more author
    IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021
    (Acceptance rate ≈ 23.70% (1663/7015))
  5. MedIA
    Deep Model-based Magnetic Resonance Parameter Mapping Network (DOPAMINE) for Fast T1 Mapping using Variable Flip Angle Method
    Yohan Jun, Hyungseob Shin, Taejoon Eo, and 2 more authors
    Medical Image Analysis, 2021
    (JCR2020 IF=8.545)
  6. RSNA
    Explainable and fully automated clinical referral suggestion for mass like lesions in brain using multi-contrast MRI
    Hyungseob Shin*, Ji Eun Park*, Yohan Jun, and 2 more authors
    In The 107th Radiological Society of North America (RSNA) Annual Meeting, 2021
    Digital Poster Presentation
  7. MedIA
    Accelerating Cartesian MRI by Domain-Transform Manifold Learning in Phase-Encoding Direction
    Taejoon Eo*, Hyungseob Shin*, Yohan Jun, and 2 more authors
    Medical Image Analysis, 2020
    (JCR2019 IF=11.148)
  8. JKSR
    The Latest Trends in Attention Mechanisms and Their Application in Medical Imaging.
    Hyungseob Shin*, Jeongryong Lee*, Taejoon Eo, and 3 more authors
    Journal of the Korean Society of Radiology, 2020