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Found 7 papers, 6 papers with code
T1/T2 relaxation temporal modelling from accelerated acquisitions using a Latent Transformer
Quantitative cardiac magnetic resonance T1 and T2 mapping enable myocardial tissue characterisation but the lengthy scan times restrict their widespread clinical application.
LesionMix: A Lesion-Level Data Augmentation Method for Medical Image Segmentation
Data augmentation has become a de facto component of deep learning-based medical image segmentation methods.
M-FLAG: Medical Vision-Language Pre-training with Frozen Language Models and Latent Space Geometry Optimization
The proposed method, named Medical vision-language pre-training with Frozen language models and Latent spAce Geometry optimization (M-FLAG), leverages a frozen language model for training stability and efficiency and introduces a novel orthogonality loss to harmonize the latent space geometry.
Feature-Conditioned Cascaded Video Diffusion Models for Precise Echocardiogram Synthesis
So far, video generation has only been possible by providing input data that is as rich as the output data, e. g., image sequence plus conditioning in, video out.
CHeart: A Conditional Spatio-Temporal Generative Model for Cardiac Anatomy
Two key questions in cardiac image analysis are to assess the anatomy and motion of the heart from images; and to understand how they are associated with non-imaging clinical factors such as gender, age and diseases.
Generative Modelling of the Ageing Heart with Cross-Sectional Imaging and Clinical Data
Understanding the morphological and functional changes of the heart during ageing is a key scientific question, the answer to which will help us define important risk factors of cardiovascular disease and monitor disease progression.
Subject-Specific Lesion Generation and Pseudo-Healthy Synthesis for Multiple Sclerosis Brain Images
In this work, we present a novel foreground-based generative method for modelling the local lesion characteristics that can both generate synthetic lesions on healthy images and synthesize subject-specific pseudo-healthy images from pathological images.
