Jiwoong Jason Jeong
Orcid: 0000-0001-5630-9443
According to our database1,
Jiwoong Jason Jeong
authored at least 15 papers
between 2019 and 2024.
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Bibliography
2024
Knowledge-Grounded Adaptation Strategy for Vision-Language Models: Building a Unique Case-Set for Screening Mammograms for Residents Training.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2024, 2024
2023
Developing an Echocardiography-Based, Automatic Deep Learning Framework for the Differentiation of Increased Left Ventricular Wall Thickness Etiologies.
J. Imaging, 2023
2022
Systematic Review of Generative Adversarial Networks (GANs) for Medical Image Classification and Segmentation.
J. Digit. Imaging, 2022
The EMory BrEast imaging Dataset (EMBED): A Racially Diverse, Granular Dataset of 3.5M Screening and Diagnostic Mammograms.
CoRR, 2022
2021
CoRR, 2021
Ultrasound multi-needle detection using deep attention U-Net with TV regularizations.
Proceedings of the Medical Imaging 2021: Image-Guided Procedures, 2021
Post-op brain tumor bed detection and segmentation using 3D Mask R-CNN for dynamic magnetic resonance perfusion imaging.
Proceedings of the Medical Imaging 2021: Biomedical Applications in Molecular, 2021
2020
Multi-Needle Detection in 3D Ultrasound Images Using Unsupervised Order-Graph Regularized Sparse Dictionary Learning.
IEEE Trans. Medical Imaging, 2020
Was there COVID-19 back in 2012? Challenge for AI in Diagnosis with Similar Indications.
CoRR, 2020
Weakly non-rigid MR-TRUS prostate registration using fully convolutional and recurrent neural networks.
Proceedings of the Medical Imaging 2020: Image Processing, 2020
Brain tumor segmentation using 3D mask R-CNN for dynamic susceptibility contrast enhanced perfusion imaging.
Proceedings of the Medical Imaging 2020: Biomedical Applications in Molecular, 2020
2019
Automatic MRI prostate segmentation using 3D deeply supervised FCN with concatenated atrous convolution.
Proceedings of the Medical Imaging 2019: Computer-Aided Diagnosis, San Diego, 2019
Machine-learning-based classification of Glioblastoma using MRI-based radiomic features.
Proceedings of the Medical Imaging 2019: Computer-Aided Diagnosis, San Diego, 2019
Machine-learning based classification of glioblastoma using dynamic susceptibility enhanced MR image.
Proceedings of the Medical Imaging 2019: Biomedical Applications in Molecular, 2019