Deep learning approaches for data augmentation in medical imaging: a review
A Kebaili, J Lapuyade-Lahorgue, S Ruan - Journal of Imaging, 2023 - mdpi.com
Deep learning has become a popular tool for medical image analysis, but the limited
availability of training data remains a major challenge, particularly in the medical field where …
availability of training data remains a major challenge, particularly in the medical field where …
[HTML][HTML] A survey of emerging applications of diffusion probabilistic models in mri
Diffusion probabilistic models (DPMs) which employ explicit likelihood characterization and
a gradual sampling process to synthesize data, have gained increasing research interest …
a gradual sampling process to synthesize data, have gained increasing research interest …
Generative models improve fairness of medical classifiers under distribution shifts
Abstract Domain generalization is a ubiquitous challenge for machine learning in
healthcare. Model performance in real-world conditions might be lower than expected …
healthcare. Model performance in real-world conditions might be lower than expected …
Make-a-volume: Leveraging latent diffusion models for cross-modality 3d brain mri synthesis
Cross-modality medical image synthesis is a critical topic and has the potential to facilitate
numerous applications in the medical imaging field. Despite recent successes in deep …
numerous applications in the medical imaging field. Despite recent successes in deep …
A diffusion model predicts 3d shapes from 2d microscopy images
DJE Waibel, E Röell, B Rieck… - 2023 IEEE 20th …, 2023 - ieeexplore.ieee.org
Diffusion models are a special type of generative model, capable of synthesising new data
from a learnt distribution. We introduce DISPR, a diffusion-based model for solving the …
from a learnt distribution. We introduce DISPR, a diffusion-based model for solving the …
Memory-efficient 3d denoising diffusion models for medical image processing
Denoising diffusion models have recently achieved state-of-the-art performance in many
image-generation tasks. They do, however, require a large amount of computational …
image-generation tasks. They do, however, require a large amount of computational …
MedSyn: Text-guided Anatomy-aware Synthesis of High-Fidelity 3D CT Images
This paper introduces an innovative methodology for producing high-quality 3D lung CT
images guided by textual information. While diffusion-based generative models are …
images guided by textual information. While diffusion-based generative models are …
Diffusion models for memory-efficient processing of 3d medical images
Denoising diffusion models have recently achieved state-of-the-art performance in many
image-generation tasks. They do, however, require a large amount of computational …
image-generation tasks. They do, however, require a large amount of computational …
Denoising Diffusion Models for Memory-efficient Processing of 3D Medical Images
Denoising diffusion models have recently achieved state-of-the-art performance in many
image-generation tasks. They do, however, require a large amount of computational …
image-generation tasks. They do, however, require a large amount of computational …
PET-diffusion: Unsupervised PET enhancement based on the latent diffusion model
Positron emission tomography (PET) is an advanced nuclear imaging technique with an
irreplaceable role in neurology and oncology studies, but its accessibility is often limited by …
irreplaceable role in neurology and oncology studies, but its accessibility is often limited by …