Yohan Jun

Research Fellow
Martinos Center for Biodmedical Engineering and Harvard Medcial School

Publications

You can find my latest articles on my Google Scholar.

Preprints

  1. X Jian, Y Jun, J Cho, M Gao, X Yong, B Bilgic. NLCG-Net: A Model-Based Zero-Shot Learning Framework for Undersampled Quantitative MRI Reconstruction. arXiv preprint arXiv:2401.12004, 2024.
  2. J Cho, Y Jun, X Wang, C Kobayashi, B Bilgic. Improved Multi-shot Diffusion-Weighted MRI with Zero-Shot Self-supervised Learning Reconstruction. arXiv preprint arXiv:2308.05103, 2023.
  3. Y Jun, Y Arefeen, J Cho, S Fujita, X Wang, PE Grant, B Gagoski, C Jaimes, MS Gee*, B Bilgic*. Zero-DeepSub: Zero-Shot Deep Subspace Reconstruction for Rapid Multiparametric Quantitative MRI Using 3D-QALAS. arXiv preprint arXiv:2307.01410, 2023.
  4. H Shin, H Kim, S Kim, Y Jun, T Eo, D Hwang. SDC-UDA: Volumetric Unsupervised Domain Adaptation Framework for Slice-Direction Continuous Cross-Modality Medical Image Segmentation. arXiv preprint arXiv:2305.11012, 2023.
  5. SA Lee, H Kim, K Kim, KC Lee, K Lee, T Eo, Y Jun, D Hwang. Deep computational microscopy via physics-informed end-to-end learning with a learned forward model. *research square preprint rs-2785592, 2023.
  6. Y Jun, J Cho, X Wang, M Gee, PE Grant, B Bilgic*, B Gagoski*. SSL-QALAS: Self-Supervised Learning for Rapid Multiparameter Estimation in Quantitative MRI Using 3D-QALAS. arXiv preprint arXiv:2302.14240, 2023.
  7. H Shin, H Kim, S Kim, Y Jun, T Eo, D Hwang. COSMOS: Cross-Modality Unsupervised Domain Adaptation for 3D Medical Image Segmentation based on Target-aware Domain Translation and Iterative Self-Training. arXiv preprint arXiv:2203.16557, 2022.
  8. H Shin, H Kim, S Kim, Y Jun, T Eo, D Hwang. Self-Training Based Unsupervised Cross-Modality Domain Adaptation for Vestibular Schwannoma and Cochlea Segmentation. arXiv preprint arXiv:2109.10674, 2021.
  9. MJ Muckley*, B Riemenschneider*, …, Y Jun, H Shin, D Hwang, …, Florian Knoll. Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction. arXiv preprint arXiv:2012.06318, 2020.

Journal Articles

  1. Y Jun, Y Arefeen, J Cho, S Fujita, X Wang, PE Grant, B Gagoski, C Jaimes, MS Gee*, B Bilgic*. Zero-DeepSub: Zero-Shot Deep Subspace Reconstruction for Rapid Multiparametric Quantitative MRI Using 3D-QALAS. Magnetic Resonance in Medicine, In Press, 2024.
  2. Y Jun, J Cho, X Wang, M Gee, PE Grant, B Bilgic*, B Gagoski*. SSL-QALAS: Self-Supervised Learning for Rapid Multiparameter Estimation in Quantitative MRI Using 3D-QALAS. Magnetic Resonance in Medicine, 90(5):2019-2032, 2023.
  3. H Shin*, JE Park*, Y Jun, T Eo, J Lee, JE Kim, DH Lee, HH Moon, SI Park, S Kim, D Hwang, HS Kim. Deep learning referral suggestion and tumour discrimination using explainable artificial intelligence applied to multiparametric MRI. European Radiology, 33:5859-5870, 2023.
  4. Y Jun*, YW Park*, H Shin*, Y Shin, JR Lee, K Han, SS Ahn, SM Lim, D Hwang, SK Lee. Intelligent Noninvasive Meningioma Grading with a Fully Automatic Segmentation using Interpretable Multiparametric Deep Learning. European Radiology, 33:6124-6133, 2023.
  5. T Kim*, Y Shin*, K Kang*, K Kim*, G Kim*, Y Byeon*, …, JR Lee, G Son, T Kim, Y Jun, …, HG Kang, D Hwang, KJ Yu. Ultrathin crystalline-silicon-based strain gauges with deep learning algorithms for silent speech interfaces. Nature Communications, 13:5815, 2022.
  6. MJ Muckley*, B Riemenschneider*, …, Y Jun, H Shin, D Hwang, …, Florian Knoll. Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction, IEEE Transactions on Medical Imaging, 40(9):2306-2317, 2021.
  7. Y Jun, H Shin, T Eo, T Kim, D Hwang. Deep model-based magnetic resonance parameter mapping network (DOPAMINE) for fast T1 mapping using variable flip angle method, Medical Image Analysis, 70:102017, 2021.
  8. YW Park*, Y Jun*, Y Lee, K Han, C An, SS Ahn, D Hwang, SK Lee. Robust performance of deep learning for automatic detection and segmentation of brain metastases using three-dimensional black-blood and three-dimensional gradient echo imaging, European Radiology, 31:6686-6695, 2021.
  9. H Shin, J Lee, T Eo, Y Jun, S Kim, D Hwang. The Latest Trends in Attention Mechanisms and Their Application in Medical Imaging, Journal of the Korean Society of Radiology, 81(6):1305-1333, 2020.
  10. T Eo*, H Shin*, Y Jun, T Kim, D Hwang. Accelerating Cartesian MRI by domain-transform manifold learning in phase-encoding direction, Medical Image Analysis, 63:101689, 2020.
  11. Y Jun, T Eo, H Shin, T Kim, HJ Lee, D Hwang. Parallel imaging in time-of-flight magnetic resonance angiography using deep multistream convolutional neural networks, Magnetic Resonance in Medicine, 81(6):3840-3853, 2019.
  12. T Kim, G Kim, H Kim, HJ Yoon, T Kim, Y Jun, TH Shin, S Kang, J Cheon, D Hwang, BW Min, W Shim. Megahertz-wave-transmitting conducting polymer electrode for device-to-device integration, Nature Communications, 10:653, 2019.
  13. Y Jun, T Eo, T Kim, H Shin, D Hwang, SH Bae, YW Park, HJ Lee, BW Choi, SS Ahn. Deep-learned 3D black-blood imaging using automatic labelling technique and 3D convolutional neural networks for detecting metastatic brain tumors, Scientific Reports, 8:9450, 2018.
  14. T Eo, Y Jun, T Kim, J Jang, HJ Lee, D Hwang. KIKI-net: cross-domain convolutional neural networks for reconstructing undersampled magnetic resonance images, Magnetic Resonance in Medicine, 80(5):2188-2201, 2018.
  15. T Eo, T Kim, Y Jun, H Lee, SS Ahn, DH Kim, D Hwang. High-SNR multiple T2(*)-contrast magnetic resonance imaging using a robust denoising method based on tissue characteristics, Journal of Magnetic Resonance Imaging, 45(6):1835-1845, 2017.

Conference Papers

  1. J Cho, Y Jun, X Wang, C Kobayashi, B Bilgic. Improved Multi-shot Diffusion-Weighted MRI with Zero-Shot Self-supervised Learning Reconstruction, In International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), pp. 457-466, 2023.
  2. H Shin, H Kim, S Kim, Y Jun, T Eo, D Hwang. SDC-UDA: Volumetric Unsupervised Domain Adaptation Framework for Slice-Direction Continuous Cross-Modality Medical Image Segmentation, In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7412-7421, 2023.
  3. PM Johnson, …, H Shin, Y Jun, T Eo, S Kim, T Kim, D Hwang, …, F Knoll. Evaluation of the Robustness of Learned MR Image Reconstruction to Systematic Deviations Between Training and Test Data for the Models from the fastMRI Challenge, In International Workshop on Machine Learning for Medical Image Reconstruction (MLMIR), pp. 25-34, 2021.
  4. Y Jun, H Shin, T Eo, D Hwang. Joint Deep Model-based MR Image and Coil Sensitivity Reconstruction Network (Joint-ICNet) for Fast MRI, In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5266-5275, 2021.
  5. T Eo, H Shin, T Kim, Y Jun, D Hwang. Translation of 1D Inverse Fourier Transform of K-space to an Image Based on Deep Learning for Accelerating Magnetic Resonance Imaging, In International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), pp. 241-249, 2018.

Conference Abstracts

  1. Y Jun, Y Arefeen, J Cho, X Wang, M Gee, B Gagoski, B Bilgic. Zero-DeepSub: Zero-Shot Deep Subspace Reconstruction for Multiparametric Quantitative MRI Using QALAS, In International Society for Magnetic Resonance in Medicine (ISMRM) Annual Meeting, 2023. [Power Pitch (Oral) Presentation] [Summa Cum Laude]
  2. Y Jun, J Cho, X Wang, M Gee, PE Grant, B Gagoski, B Bilgic. SSL-QALAS: Self-Supervised Learning for Multiparametric Quantitative MRI Using QALAS, In International Society for Magnetic Resonance in Medicine (ISMRM) Annual Meeting, 2023.
  3. Y Arefeen, Y Jun, B Gagoski, B Bilgic, E Adalsteinsson. Improved T1 and T2 mapping in 3D-QALAS using temporal subspaces and Cramer-Rao-bound flip angle optimization enabled by auto-differentiation, In International Society for Magnetic Resonance in Medicine (ISMRM) Annual Meeting, 2023. [Oral Presentation]
  4. A Vurankaya, Y Jun, J Cho, B Bilgic. Self-Supervised Deep Learning Reconstruction for Highly Accelerated Diffusion Imaging, In International Society for Magnetic Resonance in Medicine (ISMRM) Annual Meeting, 2023. [Power Pitch (Oral) Presentation]
  5. X Wang, J Cho, Y Jun, B Gagoski, B Bilgic. Model-based phase-difference reconstruction for accelerated phase-based T2 mapping, In International Society for Magnetic Resonance in Medicine (ISMRM) Annual Meeting, 2023.
  6. J Cho, TH Kim, AJL Berman, Y Jun, X Wang, B Gagoski, B Bilgic. VUDU-SAGE: Efficient T2 and T2* Mapping using Joint Reconstruction for Motion-Robust, Distortion-Free, Multi-Shot, Multi-Echo EPI, In International Society for Magnetic Resonance in Medicine (ISMRM) Annual Meeting, 2023.
  7. Y Jun, Y Arefeen, J Cho, X Wang, M Gee, B Gagoski, B Bilgic. Deep Subspace Reconstruction with Zero-Shot Learning for Multiparametric Quantitative MRI, In International Society for Magnetic Resonance in Medicine (ISMRM) Workshop on Data Sampling and Image Reconstruction, 2023. [Oral Presentation]
  8. Y Arefeen, B Gagoski, Y Jun, B Bilgic, E Adalsteinsson. Improved T1 and T2 mapping in 3D-QALAS using temporal subspaces and flip angle optimization enabled by auto-differentiation, In International Society for Magnetic Resonance in Medicine (ISMRM) Workshop on Data Sampling and Image Reconstruction, 2023.
  9. X Wang, J Cho, Y Jun, B Gagoski, B Bilgic. Model-Based Phase-Difference Reconstruction for Accelerated Phase-Based T2 Mapping, In International Society for Magnetic Resonance in Medicine (ISMRM) Workshop on Data Sampling and Image Reconstruction, 2023.
  10. J Cho, TH Kim, AJL Berman, Y Jun, X Wang, B Gagoski, B Bilgic. VUDU-SAGE: Efficient T2 and T2* Mapping using Joint Reconstruction for Motion-Robust, Distortion-Free, Multi-Shot, Multi-Echo EPI, In International Society for Magnetic Resonance in Medicine (ISMRM) Workshop on Data Sampling and Image Reconstruction, 2023.
  11. Y Jun*, YW Park*, H Shin, Y Shin, JR Lee, K Han, SM Lim, SK Lee, SS Ahn, D Hwang. Interpretable Meningioma Grading and Segmentation with Multiparametric Deep Learning, In International Society for Magnetic Resonance in Medicine (ISMRM) Annual Meeting, pp. 3064, 2022.
  12. G Son, T Eo, Y Jun, H Shin, D Hwang. Joint Generation of Multi-contrast Magnetic Resonance Images and Segmentation Map Using StyleGAN2-based Generative Network, In International Society for Magnetic Resonance in Medicine (ISMRM) Annual Meeting, pp. 0102, 2022. [Oral Presentation]
  13. G Son, Y Jun, S Kim, D Hwang, T Eo. Arbitrary Missing Contrast Generation Using Multi-Contrast Generative Network with An Encoder Network, In International Society for Magnetic Resonance in Medicine (ISMRM) Annual Meeting, pp. 4308, 2022.
  14. HG Kim, KW Kim, KC Lee, TJ Eo, K Lee, Y Jun, SA Lee, D Hwang. Deep residual network with data consistency for subsampled Fourier ptychographic microscopy, In Quantitative Phase Imaging VIII, p. PC119700B. SPIE, 2022.
  15. Y Jun*, YW Park*, Y Lee, K Han, C An, SK Lee, SS Ahn, D Hwang. Deep Learning-based Automatic Detection and Segmentation of Brain Metastases Using Multi-Task Learning with 3D Black-Blood and GRE Imaging, In International Society for Magnetic Resonance in Medicine (ISMRM) Annual Meeting, pp. 0662, 2021. [Oral Presentation] [Magna Cum Laude]
  16. Y Jun, H Shin, T Eo, D Hwang. Joint Reconstruction of MR Image and Coil Sensitivity Maps using Deep Model-based Network, In International Society for Magnetic Resonance in Medicine (ISMRM) Annual Meeting, pp. 0206, 2021. [Oral Presentation] [Magna Cum Laude]
  17. B Riemenschneider, …, Y Jun, H Shin, D Hwang, F Knoll. Results of the 2020 fastMRI Brain Reconstruction Challenge, In International Society for Magnetic Resonance in Medicine (ISMRM) Annual Meeting, pp. 0063, 2021. [Oral Presentation] [Summa Cum Laude]
  18. H Shin, JE Park, Y Jun, HS Kim, D Hwang. Explainable And Fully Automated Clinical Referral Suggestion For Mass Like Lesions In The Brain Using Multi-contrast MRI, In Radiological Society of North America (RSNA) 107th Scientific Assembly and Annual Meeting, pp. SDP-NR-16, 2021.
  19. Y Jun, H Shin, T Eo, T Kim, D Hwang. Deep Model-based MR Parameter Mapping Network (DOPAMINE) for Fast MR Reconstruction, In International Society for Magnetic Resonance in Medicine (ISMRM) Annual Meeting, pp. 0988, 2020. [Oral Presentation] [Summa Cum Laude]
  20. Y Jun, H Shin, T Eo, T Kim, D Hwang. Deep Model-based Network for Fast MR Parameter Map Reconstruction, In International Society for Magnetic Resonance in Medicine (ISMRM) Workshop on Data Sampling and Image Reconstruction, 2020. [Poster Award of 2nd Place (Silver)]
  21. Y Jun, T Eo, H Shin, T Kim, H Lee, D Hwang. Parallel Imaging in Time-of-Flight Magnetic Resonance Angiography Using Deep Multi-Stream Convolutional Neural Networks, In International Society for Magnetic Resonance in Medicine (ISMRM) Annual Meeting, pp. 4659, 2019.
  22. H Shin, T Eo, Y Jun, T Kim, H Lee, D Hwang. Parallel Imaging based on k-x Domain Interpolation using Deep Neural Networks, In International Society for Magnetic Resonance in Medicine (ISMRM) Annual Meeting, pp. 4660, 2019.
  23. Y Jun, T Eo, T Kim, H Shin, D Hwang, S Bae, Y Park, H Lee, B Choi, S Ahn. Deep-learned 3D black-blood imaging using automatic labeling technique and 3D convolutional neural networks for detection of metastatic brain tumors, In International Society for Magnetic Resonance in Medicine (ISMRM) Annual Meeting, pp. 4857, 2018.
  24. H Shin, Y Jun, T Kim, T Eo, S Ahn, D Hwang. Brain Vessel Extraction without MRA / V using Deep Convolutional Neural Network, In International Society for Magnetic Resonance in Medicine (ISMRM) Annual Meeting, pp. 3171, 2018.
  25. K Bang, J Jang, Y Jun, H Jang, H Lee, D Hwang. Automatic Selection of Optimal Regularization Parameters in Compressed Sensing using No Reference Magnetic Resonance Image Quality Assessment, In International Society for Magnetic Resonance in Medicine (ISMRM) Annual Meeting, pp. 2816, 2018.
  26. T Kim, T Eo, D Park, Y Jun, D Hwang. Deep Sinogram Learning for Radial MRI: Comparison with k-space and Image Learning, In International Society for Magnetic Resonance in Medicine (ISMRM) Annual Meeting, pp. 2799, 2018.
  27. Y Jun, T Eo, H Shin, T Kim, HJ Lee, H Jang, D Hwang. Reconstruction of brain vessel signals from undersampled time-of-flight magnetic resonance angiography using deep learning, In The 21th Annual Meeting of the the Korean Society for Brain and Neural Sciences (KSBNS), pp. 1097, 2018.
  28. Y Jun, T Eo, T Kim, J Jang, D Hwang. Deep Convolutional Neural Network for Acceleration of Magnetic Resonance Angiography (MRA), In International Society for Magnetic Resonance in Medicine (ISMRM) Annual Meeting, pp. 0686, 2017. [Oral Presentation] [Summa Cum Laude]
  29. T Eo, Y Jun, T Kim, J Jang, D Hwang. Cascaded Convolutional Neural Network (CNN) for Reconstruction of Undersampled Magnetic Resonance (MR) Images, In International Society for Magnetic Resonance in Medicine (ISMRM) Annual Meeting, pp. 3974, 2017. [Summa Cum Laude]