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Found 7 papers, 1 papers with code
MaxFusion: Plug&Play Multi-Modal Generation in Text-to-Image Diffusion Models
Large diffusion-based Text-to-Image (T2I) models have shown impressive generative powers for text-to-image generation as well as spatially conditioned image generation.
Ambiguous Medical Image Segmentation using Diffusion Models
Collective insights from a group of experts have always proven to outperform an individual's best diagnostic for clinical tasks.
Orientation-guided Graph Convolutional Network for Bone Surface Segmentation
Due to imaging artifacts and low signal-to-noise ratio in ultrasound images, automatic bone surface segmentation networks often produce fragmented predictions that can hinder the success of ultrasound-guided computer-assisted surgical procedures.
Simultaneous Bone and Shadow Segmentation Network using Task Correspondence Consistency
Segmenting both bone surface and the corresponding acoustic shadow are fundamental tasks in ultrasound (US) guided orthopedic procedures.
Image Generation with Multimodal Priors using Denoising Diffusion Probabilistic Models
Based on the fact that the distribution over each time step in the diffusion model is Gaussian, in this work we show that there exists a closed-form expression to the generate the image corresponds to the given modalities.
Realistic Ultrasound Image Synthesis for Improved Classification of Liver Disease
With the success of deep learning-based methods applied in medical image analysis, convolutional neural networks (CNNs) have been investigated for classifying liver disease from ultrasound (US) data.
Network Architecture Search for Face Enhancement
Poor quality face images often reduce the performance of face analysis and recognition systems.
