| Relevance-cam: Your model already knows where to look JR Lee, S Kim, I Park, T Eo, D Hwang Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 67 | 2021 |
| Fine-grain segmentation of the intervertebral discs from MR spine images using deep convolutional neural networks: BSU-Net S Kim, WC Bae, K Masuda, CB Chung, D Hwang Applied sciences 8 (9), 1656, 2018 | 56 | 2018 |
| Quantitative magnetic resonance imaging of the lumbar intervertebral discs D Hwang, S Kim, NA Abeydeera, S Statum, K Masuda, CB Chung, ... Quantitative imaging in Medicine and surgery 6 (6), 744, 2016 | 29 | 2016 |
| COSMOS: cross-modality unsupervised domain adaptation for 3D medical image segmentation based on target-aware domain translation and iterative self-training H Shin, H Kim, S Kim, Y Jun, T Eo, D Hwang arXiv preprint arXiv:2203.16557, 2022 | 23 | 2022 |
| SDC-UDA: volumetric unsupervised domain adaptation framework for slice-direction continuous cross-modality medical image segmentation H Shin, H Kim, S Kim, Y Jun, T Eo, D Hwang Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 21 | 2023 |
| Semi-automatic segmentation of vertebral bodies in MR images of human lumbar spines S Kim, WC Bae, K Masuda, CB Chung, D Hwang Applied sciences 8 (9), 1586, 2018 | 19 | 2018 |
| Deep-learned spike representations and sorting via an ensemble of auto-encoders J Eom, IY Park, S Kim, H Jang, S Park, Y Huh, D Hwang Neural Networks 134, 131-142, 2021 | 18 | 2021 |
| Deep‐learned short tau inversion recovery imaging using multi‐contrast MR images S Kim, H Jang, J Jang, YH Lee, D Hwang Magnetic resonance in medicine 84 (6), 2994-3008, 2020 | 18 | 2020 |
| Fat-saturated image generation from multi-contrast MRIs using generative adversarial networks with Bloch equation-based autoencoder regularization S Kim, H Jang, S Hong, YS Hong, WC Bae, S Kim, D Hwang Medical image analysis 73, 102198, 2021 | 15 | 2021 |
| Evaluation of the robustness of learned MR image reconstruction to systematic deviations between training and test data for the models from the fastMRI challenge PM Johnson, G Jeong, K Hammernik, J Schlemper, C Qin, J Duan, ... Machine Learning for Medical Image Reconstruction: 4th International …, 2021 | 11 | 2021 |
| The Latest Trends in Attention Mechanisms and Their Application in Medical Imaging. H Shin, J Lee, T Eo, Y Jun, S Kim, D Hwang Journal of the Korean Society of Radiology 81 (6), 2020 | 4 | 2020 |
| Learning radiologist’s step-by-step skill for cervical spinal injury examination: Line drawing, prevertebral soft tissue thickness measurement, and swelling detection YH Lee, S Kim, JS Suh, D Hwang IEEE Access 6, 55492-55500, 2018 | 4 | 2018 |
| An automatic icd coding network using partition-based label attention D Kim, H Yoo, S Kim arXiv preprint arXiv:2211.08429, 2022 | 2 | 2022 |
| Study on discrimination of Alzheimer’s disease states using an ensemble neural network’s model J Eom, H Jang, S Kim, J Jang, D Hwang Medical Imaging 2019: Computer-Aided Diagnosis 10950, 584-589, 2019 | 2 | 2019 |
| Automatic delicate segmentation of the intervertebral discs from MR spine images using deep convolutional neural networks: ICU-net S Kim, WC Bae, D Hwang Proceedings of the 26th Annual Meeting of ISMRM, Paris, France, 16-21, 2018 | 1 | 2018 |
| Beam hardening correction using length linearization D Oh, S Kim, D Park, D Hwang Medical Imaging 2017: Physics of Medical Imaging 10132, 655-660, 2017 | 1 | 2017 |
| Arbitrary Missing Contrast Generation Using Multi-Contrast Generative Network with An Encoder Network G Son, Y Jun, S Kim, D Hwang, T Eo | | |
| Are Two MR Images Enough to Generate the Third One Accurately?-Clinically Feasible Fat Suppression of Lumbar Spine MRI from T1w and T2w Images Only S Kim, H Jang, K Kim, HG Kim, YH Lee, S Kim, D Hwang | | |
| Generative Adversarial Network for T2-Weighted Fat Saturation MR Image Synthesis Using Bloch Equation-based Autoencoder Regularization S Kim, H Jang, S Hong, YS Hong, WC Bae, S Kim, D Hwang | | |
| Multi-Contrast-Specific Objective Functions for MR Image Deep Learning-Losses for Pixelwise Error, Misregistration, and Local Variance H Jang, S Kim, J Jang, YH Lee, D Hwang | | |
