Diffusion Probabilistic Models beat GANs on Medical Images
- URL: http://arxiv.org/abs/2212.07501v1
- Date: Wed, 14 Dec 2022 20:46:50 GMT
- Title: Diffusion Probabilistic Models beat GANs on Medical Images
- Authors: Gustav M\"uller-Franzes, Jan Moritz Niehues, Firas Khader, Soroosh
Tayebi Arasteh, Christoph Haarburger, Christiane Kuhl, Tianci Wang, Tianyu
Han, Sven Nebelung, Jakob Nikolas Kather, Daniel Truhn
- Abstract summary: We propose Medfusion, a conditional latent DDPM for medical images.
We compare our DDPM-based model against GAN-based models, which constitute the current state-of-the-art in the medical domain.
Our study shows that DDPM are a superior alternative to GANs for image synthesis in the medical domain.
- Score: 0.13386555802329278
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The success of Deep Learning applications critically depends on the quality
and scale of the underlying training data. Generative adversarial networks
(GANs) can generate arbitrary large datasets, but diversity and fidelity are
limited, which has recently been addressed by denoising diffusion probabilistic
models (DDPMs) whose superiority has been demonstrated on natural images. In
this study, we propose Medfusion, a conditional latent DDPM for medical images.
We compare our DDPM-based model against GAN-based models, which constitute the
current state-of-the-art in the medical domain. Medfusion was trained and
compared with (i) StyleGan-3 on n=101,442 images from the AIROGS challenge
dataset to generate fundoscopies with and without glaucoma, (ii) ProGAN on
n=191,027 from the CheXpert dataset to generate radiographs with and without
cardiomegaly and (iii) wGAN on n=19,557 images from the CRCMS dataset to
generate histopathological images with and without microsatellite stability. In
the AIROGS, CRMCS, and CheXpert datasets, Medfusion achieved lower (=better)
FID than the GANs (11.63 versus 20.43, 30.03 versus 49.26, and 17.28 versus
84.31). Also, fidelity (precision) and diversity (recall) were higher (=better)
for Medfusion in all three datasets. Our study shows that DDPM are a superior
alternative to GANs for image synthesis in the medical domain.
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