10 citations · 32 across the 12 of their papers we have counts for
13 papers
Blind Ultrasound Image Enhancement via Self-Supervised Physics-Guided Degradation Modeling
Shujaat Khan, Syed Muhammad Atif, Jaeyoung Huh +1
Ultrasound (US) interpretation is hampered by multiplicative speckle, acquisition blur from the point-spread function (PSF), and scanner- and operator-dependent artifacts. Supervis…
Breast Ultrasound Report Generation using LangChain
Jaeyoung Huh, Hyun Jeong Park, Jong Chul Ye
Breast ultrasound (BUS) is a critical diagnostic tool in the field of breast imaging, aiding in the early detection and characterization of breast abnormalities. Interpreting breas…
Improving Medical Speech-to-Text Accuracy with Vision-Language Pre-training Model
Jaeyoung Huh, Sangjoon Park, Jeong Eun Lee +1
Automatic Speech Recognition (ASR) is a technology that converts spoken words into text, facilitating interaction between humans and machines. One of the most common applications o…
Phase Aberration Robust Beamformer for Planewave US Using Self-Supervised Learning
Shujaat Khan, Jaeyoung Huh, Jong Chul Ye
Ultrasound (US) is widely used for clinical imaging applications thanks to its real-time and non-invasive nature. However, its lesion detectability is often limited in many applica…
Tunable Image Quality Control of 3-D Ultrasound using Switchable CycleGAN
Jaeyoung Huh, Shujaat Khan, Sungjin Choi +3
In contrast to 2-D ultrasound (US) for uniaxial plane imaging, a 3-D US imaging system can visualize a volume along three axial planes. This allows for a full view of the anatomy,…
Missing Cone Artifacts Removal in ODT using Unsupervised Deep Learning in Projection Domain
Hyungjin Chung, Jaeyoung Huh, Geon Kim +2
Optical diffraction tomography (ODT) produces three dimensional distribution of refractive index (RI) by measuring scattering fields at various angles. Although the distribution of…
Switchable Deep Beamformer
Shujaat Khan, Jaeyoung Huh, Jong Chul Ye
Recent proposals of deep beamformers using deep neural networks have attracted significant attention as computational efficient alternatives to adaptive and compressive beamformers…
OT-driven Multi-Domain Unsupervised Ultrasound Image Artifact Removal using a Single CNN
Jaeyoung Huh, Shujaat Khan, Jong Chul Ye
Ultrasound imaging (US) often suffers from distinct image artifacts from various sources. Classic approaches for solving these problems are usually model-based iterative approaches…
Pushing the Limit of Unsupervised Learning for Ultrasound Image Artifact Removal
Shujaat Khan, Jaeyoung Huh, Jong Chul Ye
Ultrasound (US) imaging is a fast and non-invasive imaging modality which is widely used for real-time clinical imaging applications without concerning about radiation hazard. Unfo…
Adaptive and Compressive Beamforming Using Deep Learning for Medical Ultrasound
Shujaat Khan, Jaeyoung Huh, Jong Chul Ye
In ultrasound (US) imaging, various types of adaptive beamforming techniques have been investigated to improve the resolution and contrast-to-noise ratio of the delay and sum (DAS)…
Deep Learning-based Universal Beamformer for Ultrasound Imaging
Shujaat Khan, Jaeyoung Huh, Jong Chul Ye
In ultrasound (US) imaging, individual channel RF measurements are back-propagated and accumulated to form an image after applying specific delays. While this time reversal is usua…
Universal Deep Beamformer for Variable Rate Ultrasound Imaging
Shujaat Khan, Jaeyoung Huh, Jong Chul Ye
Ultrasound (US) imaging is based on the time-reversal principle, in which individual channel RF measurements are back-propagated and accumulated to form an image after applying spe…
Efficient B-mode Ultrasound Image Reconstruction from Sub-sampled RF Data using Deep Learning
Yeo Hun Yoon, Shujaat Khan, Jaeyoung Huh +1
In portable, three dimensional, and ultra-fast ultrasound imaging systems, there is an increasing demand for the reconstruction of high quality images from a limited number of radi…