International Journal

 

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Graphical Abstract-BlochGAN.tif

[BlochGAN]

Fat-saturated Image Generation from Multi-contrast MRIs Using Generative Adversarial Networks with Bloch Equation-based Autoencoder Regularization. Medical Image Analysis, Volume 73, October 2021, 102198

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Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction. -with Facebook AI & NYU.  IEEE Transactions on Medical Imaging, (In press)

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Relevance CAM Relevance weighted Class A

Lee, J., Kim, S., Park, I., Eo, T., Hwang, D. (2021). Relevance-CAM: Your Model Already Knows Where to Look.  Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021, pp. 14944-14953

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Joint Deep Model-based MR Image and Coil

Jun, Y., Shin, H., Eo, T., Hwang, D. (2021). Joint Deep Model-based MR Image and Coil Sensitivity Reconstruction Network (Joint-ICNet) for Fast MRI. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021, pp. 5270-5279

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Deep Model-based Magnetic Resonance Para

Jun, Y., Shin, H., Eo, T., Kim, T., Hwang, D. (2021). Deep Model-based Magnetic Resonance Parameter Mapping Network (DOPAMINE) for Fast T1 Mapping Using Variable Flip Angle Method. Medical Image Analysis,  
70, 102017

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Park, Y.*, Jun, Y.*, Lee,  Y. , Han, K., An, C., Ahn, S.**, Hwang, D.**, Lee, S.(2021). 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, ( In press )

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Shin, H., Lee, J., Eo, T., Jun, Y., Kim, S., Hwang, D. (2020). The Latest Trends in Attention Mechanisms and Their Application in Medical Imaging. Journal of the Korean Society of Radiology, 81(6), 1305-1333.

 

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Eom, J., Park, I., Kim, S., Jang, H., Hwang, D. (2021). Deep-learned Spike Representations and Sorting via an Ensemble of Auto-encoders. Neural Networks, 134, 131-142.

 

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Deep-learned Short Tau Inversion Recover

Kim, S., Jang, H., Jang, J., Lee, Y., Hwang, D. (2020). Deep-learned Short Tau

Inversion Recovery Imaging Using Multi Contrast Magnetic Resonance Images. Magnetic Resonance in Medicine, 84(6), 2994-3008.

 

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Accelerating Cartesian MRI by Domain-Tra

Eo, T., Shin, H., Jun, Y., Kim, T., Hwang, D. (2020). Accelerating Cartesian MRI by Domain-Transform Manifold Learning in Phase-Encoding Direction. Medical Image Analysis, 63, 101689.

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Dynamic Range Expansion Using Cumulative

Jang, H., Bang, K., Jang, J., Hwang, D. (2020). Dynamic Range Expansion Using Cumulative Histogram Learning for High Dynamic Range Image Generation. IEEE Access, 8, 38554-38567.

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Deep Learning-Based Template Matching Sp

Park, I., Eom, J., Jang, H., Kim, S., Park, S., Huh, Y., Hwang, D. (2019). Deep Learning-Based Template Matching Spike Classification for Extracellular Recordings. Applied Sciences, 10(1), 301.

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Special Features on Intelligence Imaging

Hwang, D., Kim, D. (2019). Special Features on Intelligent Imaging and AnalysisApplied Sciences, 9(22), 4804.

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Megahertz-wave-transmitting conducting p

Kim, T., Kim, G., Kim, H., Yoon, H., Kim, T., Jun, Y., Shin, T., Kang, S., Cheon, J., Hwang, D., Min, B., Shim, W. (2019). Megahertz-wave-transmitting conducting polymer electrode for device-to-device integration. Nature Communications, 10(1), 653.

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Parallel Imaging in Time-of-Flight Magne

Jun, Y., Eo, T., Shin, H., Kim, T., Lee, H., Hwang, D. (2019). Parallel Imaging in Time-of-Flight Magnetic Resonance Angiography Using Deep Multi-Stream Convolutional Neural Networks. Magnetic Resonance in Medicine, 81(6), 3840-3853.

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Learning_Radiologist’s_Step-by-Step_Skil

Lee, Y., Kim, S., Suh, J., Hwang, D. (2018). Learning Radiologist’s Step-by-Step Skill for Cervical Spinal Injury Examination: Line drawing, Prevertebral Soft Tissue Thickness Measurement, and Detection of the Swelling in Radiographs. IEEE Access, 6, 55492-55500.

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Inverse Tone Mapping Operator Using Sequ

Jang, H., Bang, K., Jang, J., Hwang, D. (2018). Inverse Tone Mapping Operator Using Sequential Deep Neural Networks Based on Human Visual System. IEEE Access, 6, 52058-52072.

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Fine-grain segmentation of the intervert

Kim, S., Bae, W., Masuda, K., Chung, C., and Hwang, D. (2018). Fine-Grain Segmentation of the Intervertebral Discs from MR Spine Images Using Deep Convolutional Neural Networks: BSU-Net. Applied Sciences, 8(9), 1656.

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No-reference automatic quality assessmen

Jang, J., Jang, H., Eo, T., Bang, K., and Hwang, D. (2018). No-reference Automatic Quality Assessment for Colorfulness-Adjusted, Contrast-Adjusted, and Sharpness-Adjusted Images Using High-Dynamic-Range-Derived Features. Applied Sciences, 8(9), 1688.

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Semi-Automatic Segmentation of Vertebral

Kim, S., Bae, W., Masuda, K., Chung, C., Hwang, D. (2018). Semi-Automatic Segmentation of Vertebral Bodies in MR images of Human Lumbar Spines. Applied Sciences. 8(9), 1586. 

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