International Journal
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M3T: three-dimensional Medical image classifier using Multi-plane and Multi-slice Transformer, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022
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Importance of CT image normalization in radiomics analysis: prediction of 3-year recurrence-free survival in non-small cell lung cancer. European Radiology, 2022.
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[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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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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Quantitative analysis of the mouth opening movement of temporomandibular joint disorder patients according to disc position using computer vision: a pilot study. QUANTITATIVE IMAGING IN MEDICINE AND SURGERY , 2021.
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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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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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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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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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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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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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Hwang, D., Kim, D. (2019). Special Features on Intelligent Imaging and Analysis. Applied Sciences, 9(22), 4804.
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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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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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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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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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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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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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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.