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Efficient Semantic Diffusion Architectures for Model Training on Synthetic Echocardiograms
David Stojanovski, Mariana da Silva, Pablo Lamata +2
We investigate the utility of diffusion generative models to efficiently synthesise datasets that effectively train deep learning models for image analysis. Specifically, we propos…
Robustness Testing of Black-Box Models Against CT Degradation Through Test-Time Augmentation
Jack Highton, Quok Zong Chong, Samuel Finestone +3
Deep learning models for medical image segmentation and object detection are becoming increasingly available as clinical products. However, as details are rarely provided about the…
Echo from noise: synthetic ultrasound image generation using diffusion models for real image segmentation
David Stojanovski, Uxio Hermida, Pablo Lamata +2
We propose a novel pipeline for the generation of synthetic ultrasound images via Denoising Diffusion Probabilistic Models (DDPMs) guided by cardiac semantic label maps. We show th…