Latest AI and machine learning research in psoriasis for healthcare professionals.
Diffusion models are widely used in applications ranging from image generation to inverse problems. However, training diffusion models typically requires clean ground-truth images, which are unavailable in many applications. We introduce the Measurement Score-based diffusion Model (MSM), a novel framework that learns partial measurement scores using only noisy and subsampled measurements. MSM mo...
Parameter estimation in inverse problems involving partial differential equations (PDEs) underpins modeling across scientific disciplines, especially when parameters vary in space or time. Physics-informed Machine Learning (PhiML) integrates PDE constraints into deep learning, but prevailing approaches depend on recursive automatic differentiation (autodiff), which produces inaccurate high-order...
Diffusion models are widely used as priors in imaging inverse problems. However, their performance often degrades under distribution shifts between ...
BACKGROUND: Psoriasis, a chronic immune-mediated inflammatory disease (IMID), presents significant therapeutic challenges, necessitating exploration o...
The field of text-to-image generation has undergone significant advancements with the introduction of diffusion models. Nevertheless, the challenge ...
In imaging inverse problems, we would like to know how close the recovered image is to the true image in terms of full-reference image quality (FRIQ...
Recent advances in artificial intelligence (AI) and multimodal data collection are revolutionizing dermatology. Generative AI and machine learning a...
Recent advances in artificial intelligence (AI) and multimodal data collection are revolutionizing dermatology. Generative AI and machine learning app...
Waveform inversion seeks to estimate an inaccessible heterogeneous medium from data gathered by sensors that emit probing signals and measure the ge...
Plug-and-play (PnP) methods with deep denoisers have shown impressive results in imaging problems. They typically require strong convexity or smooth...
A new approach for solving the optical inverse problem of quantitative photoacoustic tomography is introduced, which interpolates between the well-k...
Solving non-convex regularized inverse problems is challenging due to their complex optimization landscapes and multiple local minima. However, thes...
Inertial Measurement Units (IMUs) enable portable, multibody motion capture (MoCap) in diverse environments beyond the laboratory, making them a pra...
Fuzzy graph theory, with its ability to handle uncertainty and varying relationship strengths, offers a powerful tool for modeling and solving complex...
Identifying host defense peptides (HDPs) that are effective against drug-resistant infections is challenging due to their vast sequence space. Artific...
Estimating parameters from samples of an optimal probability distribution is essential in applications ranging from socio-economic modeling to biolo...
In this book chapter, we discuss recent advances in data-driven approaches for inverse problems. In particular, we focus on the \emph{paired autoenc...
Current immunotherapeutic approaches for autoimmune disorders primarily rely on the use of generalized immunosuppressive medications. However, most im...
Rotator cuff tears are a common cause of shoulder pain and dysfunction, affecting up to 33% of the population, and approximately 250,000 arthroscopic ...
Multi-target inverse design, which involves designing multiple targets with different optimization objectives, becomes a key focus in mechanical metam...