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Found 12 papers, 2 papers with code
Breast Ultrasound Report Generation using LangChain
Breast ultrasound (BUS) is a critical diagnostic tool in the field of breast imaging, aiding in the early detection and characterization of breast abnormalities.
Improving Medical Speech-to-Text Accuracy with Vision-Language Pre-training Model
Automatic Speech Recognition (ASR) is a technology that converts spoken words into text, facilitating interaction between humans and machines.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Phase Aberration Robust Beamformer for Planewave US Using Self-Supervised Learning
Ultrasound (US) is widely used for clinical imaging applications thanks to its real-time and non-invasive nature.
Tunable Image Quality Control of 3-D Ultrasound using Switchable CycleGAN
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.
Missing Cone Artifacts Removal in ODT using Unsupervised Deep Learning in Projection Domain
Optical diffraction tomography (ODT) produces three dimensional distribution of refractive index (RI) by measuring scattering fields at various angles.
Switchable Deep Beamformer
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
Unfortunately, in the current deep learning approaches, a dedicated neural network should be trained with matched training data for each specific artifact type.
Pushing the Limit of Unsupervised Learning for Ultrasound Image Artifact Removal
Experimental results for various tasks such as deconvolution, speckle removal, limited data artifact removal, etc.
Adaptive and Compressive Beamforming Using Deep Learning for Medical Ultrasound
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) beamformers.
Deep Learning-based Universal Beamformer for Ultrasound Imaging
In ultrasound (US) imaging, individual channel RF measurements are back-propagated and accumulated to form an image after applying specific delays.
Universal Deep Beamformer for Variable Rate Ultrasound Imaging
In particular, we design an end-to-end deep learning framework that can directly process sub-sampled RF data acquired at different subsampling rate and detector configuration to generate high quality ultrasound images using a single beamformer.
Efficient B-mode Ultrasound Image Reconstruction from Sub-sampled RF Data using Deep Learning
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 radio-frequency (RF) measurements due to receiver (Rx) or transmit (Xmit) event sub-sampling.
