2022
DOI: 10.1007/978-3-031-18576-2_12
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Brain Imaging Generation with Latent Diffusion Models

Abstract: Deep neural networks have brought remarkable breakthroughs in medical image analysis. However, due to their data-hungry nature, the modest dataset sizes in medical imaging projects might be hindering their full potential. Generating synthetic data provides a promising alternative, allowing to complement training datasets and conducting medical image research at a larger scale. Diffusion models recently have caught the attention of the computer vision community by producing photorealistic synthetic images. In t… Show more

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Cited by 154 publications
(47 citation statements)
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References 27 publications
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“…Image generation lungs X-Ray, CT, MRI [2,5,16,17] Image segmentation MRI, CT, ultrasound [9,13,30] Image inpainting MRI [22] Image denoising MRI, CT, retinal OCT [6,11,32] Lesion detection MRI [24,29,31] Image translation MRI, CT [13,15] Seed-image based augmentation Dermatology [23] Skin disease classification Dermatology This work using large synthetic datasets Inspired by the recent early success of DPMs, we propose to use diffusion models for image augmentation as part of supervised machine learning pipelines. More specifically, we study how diffusion models can i) increase the classification metrics for skin diseases, and ii) augment skin condition datasets by effectively manipulating the generated images' features conditioned on the input text prompts.…”
Section: Medical Applications Dataset Domain Papersmentioning
confidence: 99%
“…Image generation lungs X-Ray, CT, MRI [2,5,16,17] Image segmentation MRI, CT, ultrasound [9,13,30] Image inpainting MRI [22] Image denoising MRI, CT, retinal OCT [6,11,32] Lesion detection MRI [24,29,31] Image translation MRI, CT [13,15] Seed-image based augmentation Dermatology [23] Skin disease classification Dermatology This work using large synthetic datasets Inspired by the recent early success of DPMs, we propose to use diffusion models for image augmentation as part of supervised machine learning pipelines. More specifically, we study how diffusion models can i) increase the classification metrics for skin diseases, and ii) augment skin condition datasets by effectively manipulating the generated images' features conditioned on the input text prompts.…”
Section: Medical Applications Dataset Domain Papersmentioning
confidence: 99%
“…These simulations are obtained using standard finite difference discretisation of the space and time-derivatives, generating a simplistic approach that performs well [2]. Such models and method allow to generate realistic synthetic medical images that can be used for data augmentation [10,49].…”
Section: Inverse Problems For Tumour Growthmentioning
confidence: 99%
“…However, it has been reported that GANs tend to produce clearer images than VAEs in diffusion‐weighted and T1‐weighted images (Treder et al, 2022 ). More recently, latent diffusion models have been proposed for generating synthetic images (Pinaya et al, 2022 ).…”
Section: Related Workmentioning
confidence: 99%