Psoriasis

CPFI-EIT: A CNN-PINN Framework for Full-Inverse Electrical Impedance Tomography on Non-Smooth Conductivity Distributions

This paper introduces a hybrid learning framework that combines convolutional neural networks (CNNs) and physics-informed neural networks (PINNs) to address the challenging problem of full-inverse electrical impedance tomography (EIT). EIT is a noninvasive imaging technique that reconstructs the spatial distribution of internal conductivity based on boundary voltage measurements from injected cu...

Inverting Transformer-based Vision Models

Understanding the mechanisms underlying deep neural networks in computer vision remains a fundamental challenge. While many previous approaches have focused on visualizing intermediate representations within deep neural networks, particularly convolutional neural networks, these techniques have yet to be thoroughly explored in transformer-based vision models. In this study, we apply a modular ap...

Unsupervised Multi-Parameter Inverse Solving for Reducing Ring Artifacts in 3D X-Ray CBCT

Ring artifacts are prevalent in 3D cone-beam computed tomography (CBCT) due to non-ideal responses of X-ray detectors, substantially affecting image...

Learning Hidden Physics and System Parameters with Deep Operator Networks

Big data is transforming scientific progress by enabling the discovery of novel models, enhancing existing frameworks, and facilitating precise unce...

DAWN-FM: Data-Aware and Noise-Informed Flow Matching for Solving Inverse Problems

Inverse problems, which involve estimating parameters from incomplete or noisy observations, arise in various fields such as medical imaging, geophy...

Enhancing and Accelerating Diffusion-Based Inverse Problem Solving through Measurements Optimization

Diffusion models have recently demonstrated notable success in solving inverse problems. However, current diffusion model-based solutions typically ...

Patient-specific prediction of glioblastoma growth via reduced order modeling and neural networks

Glioblastoma is among the most aggressive brain tumors in adults, characterized by patient-specific invasion patterns driven by the underlying brain...

AI-driven Inverse Design of Band-Tunable Mechanical Metastructures for Tailored Vibration Mitigation

On-demand vibration mitigation in a mechanical system needs the suitable design of multiscale metastructures, involving complex unit cells. In this ...

Efficient Model Compression Techniques with FishLeg

In many domains, the most successful AI models tend to be the largest, indeed often too large to be handled by AI players with limited computational...

Forward and Inverse Simulation of Pseudo-Two-Dimensional Model of Lithium-Ion Batteries Using Neural Networks

In this work, we address the challenges posed by the high nonlinearity of the Butler-Volmer (BV) equation in forward and inverse simulations of the ...

Pruning the Path to Optimal Care: Identifying Systematically Suboptimal Medical Decision-Making with Inverse Reinforcement Learning

In aims to uncover insights into medical decision-making embedded within observational data from clinical settings, we present a novel application o...

Bridge-IF: Learning Inverse Protein Folding with Markov Bridges

Inverse protein folding is a fundamental task in computational protein design, which aims to design protein sequences that fold into the desired bac...

Chasing Better Deep Image Priors between Over- and Under-parameterization

Deep Neural Networks (DNNs) are well-known to act as over-parameterized deep image priors (DIP) that regularize various image inverse problems. Mean...

History-Matching of Imbibition Flow in Multiscale Fractured Porous Media Using Physics-Informed Neural Networks (PINNs)

We propose a workflow based on physics-informed neural networks (PINNs) to model multiphase fluid flow in fractured porous media. After validating t...

Multimodal Large Language Models for Inverse Molecular Design with Retrosynthetic Planning

While large language models (LLMs) have integrated images, adapting them to graphs remains challenging, limiting their applications in materials and...

Discovering Cyclists' Visual Preferences Through Shared Bike Trajectories and Street View Images Using Inverse Reinforcement Learning

Cycling has gained global popularity for its health benefits and positive urban impacts. To effectively promote cycling, early studies have extensiv...

External Testing of a Deep Learning Model to Estimate Biologic Age Using Chest Radiographs.

Purpose To assess the prognostic value of a deep learning-based chest radiographic age (hereafter, CXR-Age) model in a large external test cohort of A...

Predicting the role of the human gut microbiome in type 1 diabetes using machine-learning methods.

Gut microbes is a crucial factor in the pathogenesis of type 1 diabetes (T1D). However, it is still unclear which gut microbiota are the key factors a...

Deep-TEMPEST: Using Deep Learning to Eavesdrop on HDMI from its Unintended Electromagnetic Emanations

In this work, we address the problem of eavesdropping on digital video displays by analyzing the electromagnetic waves that unintentionally emanate ...

Solving the inverse problem of microscopy deconvolution with a residual Beylkin-Coifman-Rokhlin neural network

Optic deconvolution in light microscopy (LM) refers to recovering the object details from images, revealing the ground truth of samples. Traditional...