14 papers · ranked by Valyu relevance
Minshuo Chen, Song Mei, Jianqing Fan, Mengdi Wang
Diffusion models, a powerful and universal generative artificial intelligence technology, have achieved tremendous success and opened up new possibilities in diverse applications. In these applications, diffusion models provide flexible high-dimensional data modeling, and act as a sampler for generating new samples…
Alex Ling Yu Hung, Kai Zhao, Haoxin Zheng, Ran Yan + 7 more
Conditional image generation plays a vital role in medical image analysis as it is effective in tasks such as super-resolution, denoising, and inpainting, among others. Diffusion models have been shown to perform at a state-of-the-art level in natural image generation, but they have not been thoroughly studied in…
Alexander Scheinker
Advanced accelerator-based light sources such as free electron lasers (FEL) accelerate highly relativistic electron beams to generate incredibly short (10s of femtoseconds) coherent flashes of light for dynamic imaging, whose brightness exceeds that of traditional synchrotron-based light sources by orders of magnitude.…
Jinhee Kwak, Jaehee Jung, Khursheed Aurangzeb
Electrocardiograms (ECGs) provide essential data for diagnosing arrhythmias, which can potentially cause serious health complications. Early detection through continuous monitoring is crucial for timely intervention. The Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia dataset employed…
Haotian Chen, Yiting Shen, Jichun Li, Weizhong Zhao
Fragment-based molecular generation has emerged as a promising paradigm in structure-based drug design (SBDD), deriving effective compounds with advanced properties, including chemical validity, synthetic feasibility, pharmacological relevance, etc. However, existing approaches often struggle with generating molecules…
Erpai Luo, Minsheng Hao, Lei Wei, Xuegong Zhang + 1 more
We use the classifier guidance method to perform conditional generation. This method does not interfere with the training of the denoising network model. Instead, the classifier is first trained separately by using condition labels like cell types and then provides gradients to guide cell generation. The classifier for…
Alzahra Altalib, Chunhui Li, Alessandro Perelli, Emilio Quaia
Title: Simple Summary Cone Beam Computed Tomography (CBCT) is widely used in radiotherapy because it is fast and relatively low dose, but its image quality is lower than that of conventional CT. Conditional diffusion models have recently been proposed to convert CBCT into synthetic CT with improved anatomical detail…
Authors not listed
Goal: To accurately detect infections in Diabetic Foot Ulcers (DFUs) using photographs taken at the Point of Care (POC). Achieving high performance is critical for preventing complications and amputations, as well as minimizing unnecessary emergency department visits and referrals. Methods: This paper proposes the…
Zheng Zhao, Ziwei Luo, Jens Sjölund, Thomas Schön
Generative diffusions are a powerful class of Monte Carlo samplers that leverage bridging Markov processes to approximate complex, high-dimensional distributions, such as those found in image processing and language models. Despite their success in these domains, an important open challenge remains: extending these…
Ahmed Abotaleb, Mohamed W. Fakhr, Mohamed Zaki
Multimodal Conditioned face image generation and face super-resolution are significant areas of research. To achieve optimal results, this paper utilizes diffusion models as the primary engine for these tasks. This paper presents two main contributions: (1) “Speaking the Language of Faces” (SLF): a flexible, modular…
Yuichi Itto, Vladimir Aristov
A conditional entropic approach is discussed for nonequilibrium complex systems with a weak correlation between spatiotemporally fluctuating quantities on a large time scale. The weak correlation is found to constitute the fluctuation distribution that maximizes the entropy associated with the conditional fluctuations.…
Luca Ambrogioni, Francesco Ginelli
Generative diffusion models have achieved spectacular performance in many areas of machine learning and generative modeling. While the fundamental ideas behind these models come from non-equilibrium physics, variational inference, and stochastic calculus, in this paper we show that many aspects of these models can be…
Giulio Franzese, Pietro Michiardi
Diffusion models have recently emerged as a powerful class of generative models, achieving state-of-the-art performance in various domains such as image and audio synthesis. While most existing work focuses on finite-dimensional data, there is growing interest in extending diffusion models to infinite-dimensional…
Sukirt Thakur, Ehsan Esmaili, Sarah Libring, Luis Solorio + 1 more
'Arezoo M. Ardekani'] Resolving the diffusion coefficient is a key element in many biological and engineering systems, including pharmacological drug transport and fluid mechanics analyses. Additionally, these systems often have spatial variation in the diffusion coefficient which must be determined, such as for…