Search Results for author:
Found 16 papers, 6 papers with code
EchoFlow: A Foundation Model for Cardiac Ultrasound Image and Video Generation
Advances in deep learning have significantly enhanced medical image analysis, yet the availability of large-scale medical datasets remains constrained by patient privacy concerns.
SACB-Net: Spatial-awareness Convolutions for Medical Image Registration
Deep learning-based image registration methods have shown state-of-the-art performance and rapid inference speeds.
JVID: Joint Video-Image Diffusion for Visual-Quality and Temporal-Consistency in Video Generation
We introduce the Joint Video-Image Diffusion model (JVID), a novel approach to generating high-quality and temporally coherent videos.
EchoNet-Synthetic: Privacy-preserving Video Generation for Safe Medical Data Sharing
To make medical datasets accessible without sharing sensitive patient information, we introduce a novel end-to-end approach for generative de-identification of dynamic medical imaging data.
DeepMesh: Mesh-based Cardiac Motion Tracking using Deep Learning
We propose a novel learning framework, DeepMesh, which propagates a template heart mesh to a subject space and estimates the 3D motion of the heart mesh from CMR images for individual subjects.
Mesh-based 3D Motion Tracking in Cardiac MRI using Deep Learning
By developing a differentiable mesh-to-image rasterizer, the method is able to leverage the anatomical shape information from 2D multi-view CMR images for 3D motion estimation.
MulViMotion: Shape-aware 3D Myocardial Motion Tracking from Multi-View Cardiac MRI
To address this problem, we propose a novel multi-view motion estimation network (MulViMotion), which integrates 2D cine CMR images acquired in short-axis and long-axis planes to learn a consistent 3D motion field of the heart.
Video Summarization through Reinforcement Learning with a 3D Spatio-Temporal U-Net
Intelligent video summarization algorithms allow to quickly convey the most relevant information in videos through the identification of the most essential and explanatory content while removing redundant video frames.
Mutual Information-based Disentangled Neural Networks for Classifying Unseen Categories in Different Domains: Application to Fetal Ultrasound Imaging
To address this problem, we propose Mutual Information-based Disentangled Neural Networks (MIDNet), which extract generalizable categorical features to transfer knowledge to unseen categories in a target domain.
Unsupervised Cross-domain Image Classification by Distance Metric Guided Feature Alignment
The proposed MetFA method explicitly and directly learns the latent representation without using domain adversarial training.
Automated Detection of Congenital Heart Disease in Fetal Ultrasound Screening
In this paper we discuss the potential for deep learning techniques to aid in the detection of congenital heart disease (CHD) in fetal ultrasound.
Ultrasound Video Summarization using Deep Reinforcement Learning
We show that our method is superior to alternative video summarization methods and that it preserves essential information required by clinical diagnostic standards.
Learning Cross-domain Generalizable Features by Representation Disentanglement
Deep learning models exhibit limited generalizability across different domains.
Semi-supervised Learning of Fetal Anatomy from Ultrasound
Semi-supervised learning methods have achieved excellent performance on standard benchmark datasets using very few labelled images.
Representation Disentanglement for Multi-task Learning with application to Fetal Ultrasound
These entangled image properties lead to a semantically redundant feature encoding for the relevant task and thus lead to poor generalization of deep learning algorithms.
Weakly Supervised Estimation of Shadow Confidence Maps in Fetal Ultrasound Imaging
Our method is more consistent than human annotation, and outperforms the state-of-the-art quantitatively in shadow segmentation and qualitatively in confidence estimation of shadow regions.
