6 research outputs found
Assessing the capacity of a denoising diffusion probabilistic model to reproduce spatial context
Diffusion models have emerged as a popular family of deep generative models
(DGMs). In the literature, it has been claimed that one class of diffusion
models -- denoising diffusion probabilistic models (DDPMs) -- demonstrate
superior image synthesis performance as compared to generative adversarial
networks (GANs). To date, these claims have been evaluated using either
ensemble-based methods designed for natural images, or conventional measures of
image quality such as structural similarity. However, there remains an
important need to understand the extent to which DDPMs can reliably learn
medical imaging domain-relevant information, which is referred to as `spatial
context' in this work. To address this, a systematic assessment of the ability
of DDPMs to learn spatial context relevant to medical imaging applications is
reported for the first time. A key aspect of the studies is the use of
stochastic context models (SCMs) to produce training data. In this way, the
ability of the DDPMs to reliably reproduce spatial context can be
quantitatively assessed by use of post-hoc image analyses. Error-rates in
DDPM-generated ensembles are reported, and compared to those corresponding to a
modern GAN. The studies reveal new and important insights regarding the
capacity of DDPMs to learn spatial context. Notably, the results demonstrate
that DDPMs hold significant capacity for generating contextually correct images
that are `interpolated' between training samples, which may benefit
data-augmentation tasks in ways that GANs cannot.Comment: This paper is under consideration at IEEE TM
Adaptive Diffusion Priors for Accelerated MRI Reconstruction
Deep MRI reconstruction is commonly performed with conditional models that
de-alias undersampled acquisitions to recover images consistent with
fully-sampled data. Since conditional models are trained with knowledge of the
imaging operator, they can show poor generalization across variable operators.
Unconditional models instead learn generative image priors decoupled from the
imaging operator to improve reliability against domain shifts. Recent diffusion
models are particularly promising given their high sample fidelity.
Nevertheless, inference with a static image prior can perform suboptimally.
Here we propose the first adaptive diffusion prior for MRI reconstruction,
AdaDiff, to improve performance and reliability against domain shifts. AdaDiff
leverages an efficient diffusion prior trained via adversarial mapping over
large reverse diffusion steps. A two-phase reconstruction is executed following
training: a rapid-diffusion phase that produces an initial reconstruction with
the trained prior, and an adaptation phase that further refines the result by
updating the prior to minimize reconstruction loss on acquired data.
Demonstrations on multi-contrast brain MRI clearly indicate that AdaDiff
outperforms competing conditional and unconditional methods under domain
shifts, and achieves superior or on par within-domain performance
Unsupervised Medical Image Translation with Adversarial Diffusion Models
Imputation of missing images via source-to-target modality translation can
improve diversity in medical imaging protocols. A pervasive approach for
synthesizing target images involves one-shot mapping through generative
adversarial networks (GAN). Yet, GAN models that implicitly characterize the
image distribution can suffer from limited sample fidelity. Here, we propose a
novel method based on adversarial diffusion modeling, SynDiff, for improved
performance in medical image translation. To capture a direct correlate of the
image distribution, SynDiff leverages a conditional diffusion process that
progressively maps noise and source images onto the target image. For fast and
accurate image sampling during inference, large diffusion steps are taken with
adversarial projections in the reverse diffusion direction. To enable training
on unpaired datasets, a cycle-consistent architecture is devised with coupled
diffusive and non-diffusive modules that bilaterally translate between two
modalities. Extensive assessments are reported on the utility of SynDiff
against competing GAN and diffusion models in multi-contrast MRI and MRI-CT
translation. Our demonstrations indicate that SynDiff offers quantitatively and
qualitatively superior performance against competing baselines.Comment: M. Ozbey and O. Dalmaz contributed equally to this stud
TÜBİTAK Matematik Olimpiyatı tarihi ve Türk eğitim sistemindeki yeri
Ankara : İhsan Doğramacı Bilkent Üniversitesi İktisadi, İdari ve Sosyal Bilimler Fakültesi, Tarih Bölümü, 2015.This work is a student project of the The Department of History, Faculty of Economics, Administrative and Social Sciences, İhsan Doğramacı Bilkent University.by Pamuk, Fatih