DiffuseMorph: Unsupervised Deformable Image Registration Along Continuous Trajectory Using Diffusion Models

Kim, Boah; Han, Inwoo; Ye, Jong Chul

doi:10.48550/arxiv.2112.05149

Cited by 6 publications

(4 citation statements)

References 31 publications

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“…Diffusion models for image style transfer Diffusion models have become popular due to their impressive ability to generate high-quality images [15,33]. Diffusion models have found application in various computer vision areas, including super-resolution [30], segmentation [2], image editing [1], medical image processing [22], and video generation [16].…”

Section: Related Workmentioning

confidence: 99%

Zero-Shot Contrastive Loss for Text-Guided Diffusion Image Style Transfer

Yang¹,

Hwang²,

Ye³

2023

Preprint

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Section: Related Workmentioning

confidence: 99%

Zero-Shot Contrastive Loss for Text-Guided Diffusion Image Style Transfer

Yang¹,

Hwang²,

Ye³

2023

Preprint

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“…• Generalization to other types of noise distributions [53] • Facilitating diffusion process via implicit guidance [54] • Acceleration improvement using trainingfree DDIM sampling [55,28] • Guiding diffusion process via classifier guidance [28] • Conditional DDPMs [46,32,36,50,135,57] • Generating synthetic segmentation datasets [43,44] • Cross-modality translation [34] • Multi-modal conversion [32,34] • Exploiting K-space parameter-free guidance [38] • Accelerate MC sampling using coarse-tofine sampling [38] • Adversarial learning in the reverse diffusion process [39,34] • 3D reconstruction from 2D images [50] • Conditioning on medical meta-data [48] • Using LDM to enhance the training and sampling efficiency [48,59] • DDPMs for histopathology images [49] • 3D medical image generation [51] • Using deformation fields for medical image generation [134,52] • DDPMs in skin image adversarial attacks [56] Noise Conditioned Score Networks (NCSNs) 1 CSGM-MRI-Langevin [96] 2 Self-Score [41] In this algorithm [61], creating samples requires solving the Langevin dynamics equation. However, this equation mandates the solution of the gradient of the log density w.r.t.…”

Section: Comparative Overviewmentioning

confidence: 99%

Diffusion Models for Medical Image Analysis: A Comprehensive Survey

Kazerouni¹,

Aghdam²,

Heidari³

et al. 2022

Preprint

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Denoising diffusion models, a class of generative models, have garnered immense interest lately in various deep-learning problems. A diffusion probabilistic model defines a forward diffusion stage where the input data is gradually perturbed over several steps by adding Gaussian noise and then learns to reverse the diffusion process to retrieve the desired noise-free data from noisy data samples. Diffusion models are widely appreciated for their strong mode coverage and quality of the generated samples in spite of their known computational burdens. Capitalizing on the advances in computer vision, the field of medical imaging has also observed a growing interest in diffusion models. With the aim of helping the researcher navigate this profusion, this survey intends to provide a comprehensive overview of diffusion models in the discipline of medical image analysis. Specifically, we start with an introduction to the solid theoretical foundation and fundamental concepts behind diffusion models and the three generic diffusion modeling frameworks, namely, diffusion probabilistic models, noise-conditioned score networks, and stochastic differential equations. Then, we provide a systematic taxonomy of diffusion models in the medical domain and propose a multi-perspective categorization based on their application, imaging modality, organ of interest, and algorithms. To this end, we cover extensive applications of diffusion models in the medical domain, including segmentation, anomaly detection, image-to-image translation, 2/3D generation, reconstruction, denoising, and other medically-related challenges. Furthermore, we emphasize the practical use case of some selected approaches, and then we discuss the limitations of the diffusion models in the medical domain and propose several directions to fulfill the demands of this field. Finally, we gather the overviewed studies with their available open-source implementations at our GitHub 1 . We aim to update the relevant latest papers within it regularly.

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“…Lately, DDPMs were in focus for there ability to beat GANs on image synthesis [8]. In the flow of this success, they were also applied on image-to-image translation [23,7], segmentation [4], reconstruction [22] and registration [12]. As shown in [25], DDIMs are closely related to score-based generative models [26], which can be used for interpolation between images.…”

Section: Related Workmentioning

confidence: 99%

Diffusion Models for Medical Anomaly Detection

Wolleb¹,

Bieder²,

Sandkühler³

et al. 2022

Preprint

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In medical applications, weakly supervised anomaly detection methods are of great interest, as only image-level annotations are required for training. Current anomaly detection methods mainly rely on generative adversarial networks or autoencoder models. Those models are often complicated to train or have difficulties to preserve fine details in the image. We present a novel weakly supervised anomaly detection method based on denoising diffusion implicit models. We combine the deterministic iterative noising and denoising scheme with classifier guidance for image-to-image translation between diseased and healthy subjects. Our method generates very detailed anomaly maps without the need for a complex training procedure. We evaluate our method on the BRATS2020 dataset for brain tumor detection and the CheXpert dataset for detecting pleural effusions.

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DiffuseMorph: Unsupervised Deformable Image Registration Along Continuous Trajectory Using Diffusion Models

Abstract: Figure 1. DiffuseMorph enables deformable registration along the continuous trajectory, as well as image generation.

Cited by 6 publications

References 31 publications

Zero-Shot Contrastive Loss for Text-Guided Diffusion Image Style Transfer

Zero-Shot Contrastive Loss for Text-Guided Diffusion Image Style Transfer

Diffusion Models for Medical Image Analysis: A Comprehensive Survey

Diffusion Models for Medical Anomaly Detection

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