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OnUVS: Online Feature Decoupling Framework for High-Fidelity Ultrasound Video Synthesis
Second, to better preserve the integrity and textural information of US images, we implemented a dual-decoder that decouples the content and textural features in the generator.
Online Reflective Learning for Robust Medical Image Segmentation
Deep segmentation models often face the failure risks when the testing image presents unseen distributions.
Weakly-supervised High-fidelity Ultrasound Video Synthesis with Feature Decoupling
First, leveraging the advantages of self- and fully-supervised learning, our proposed system is trained in weakly-supervised manner for keypoint detection.
Sketch guided and progressive growing GAN for realistic and editable ultrasound image synthesis
Our main contributions include: 1) we present the first work that can synthesize realistic B-mode US images with high-resolution and customized texture editing features; 2) to enhance structural details of generated images, we propose to introduce auxiliary sketch guidance into a conditional GAN.
Contrastive Rendering for Ultrasound Image Segmentation
However, the lack of sharp boundaries in US images still remains an inherent challenge for segmentation.
Synthesis and Edition of Ultrasound Images via Sketch Guided Progressive Growing GANs
Efficiently synthesizing realistic, editable and high resolution US images can solve the problems.
