Infectious Disease

NeAS: 3D Reconstruction from X-ray Images using Neural Attenuation Surface

Reconstructing three-dimensional (3D) structures from two-dimensional (2D) X-ray images is a valuable and efficient technique in medical applications that requires less radiation exposure than computed tomography scans. Recent approaches that use implicit neural representations have enabled the synthesis of novel views from sparse X-ray images. However, although image synthesis has improved the ...

From Text to Visuals: Using LLMs to Generate Math Diagrams with Vector Graphics

Advances in large language models (LLMs) offer new possibilities for enhancing math education by automating support for both teachers and students. While prior work has focused on generating math problems and high-quality distractors, the role of visualization in math learning remains under-explored. Diagrams are essential for mathematical thinking and problem-solving, yet manually creating them...

Antibiotic Resistance Microbiology Dataset (ARMD): A De-identified Resource for Studying Antimicrobial Resistance Using Electronic Health Records

The Antibiotic Resistance Microbiology Dataset (ARMD) is a de-identified resource derived from electronic health records (EHR) that facilitates rese...

FMT:A Multimodal Pneumonia Detection Model Based on Stacking MOE Framework

Artificial intelligence has shown the potential to improve diagnostic accuracy through medical image analysis for pneumonia diagnosis. However, trad...

Screening of mitochondrial-related biomarkers connected with immune infiltration for acute respiratory distress syndrome through WGCNA and machine learning.

Septic acute respiratory distress syndrome (ARDS) is a complex and noteworthy type, but its molecular mechanism has not been fully elucidated. The aim...

MIAdapt: Source-free Few-shot Domain Adaptive Object Detection for Microscopic Images

Existing generic unsupervised domain adaptation approaches require access to both a large labeled source dataset and a sufficient unlabeled target d...

To Vaccinate or not to Vaccinate? Analyzing $\mathbb{X}$ Power over the Pandemic

The COVID-19 pandemic has profoundly affected the normal course of life -- from lock-downs and virtual meetings to the unprecedentedly swift creatio...

Goat Optimization Algorithm: A Novel Bio-Inspired Metaheuristic for Global Optimization

This paper presents the Goat Optimization Algorithm (GOA), a novel bio-inspired metaheuristic optimization technique inspired by goats' adaptive for...

APNet, an explainable sparse deep learning model to discover differentially active drivers of severe COVID-19.

MOTIVATION: Computational analyses of bulk and single-cell omics provide translational insights into complex diseases, such as COVID-19, by revealing ...

MOSTPLAS: a self-correction multi-label learning model for plasmid host range prediction.

MOTIVATION: Plasmids play an essential role in horizontal gene transfer, aiding their host bacteria in acquiring beneficial traits like antibiotic and...

PharaCon: a new framework for identifying bacteriophages via conditional representation learning.

MOTIVATION: Identifying bacteriophages (phages) within metagenomic sequences is essential for understanding microbial community dynamics. Transformer-...

Diagnosis of Patients with Viral, Bacterial, and Non-Pneumonia Based on Chest X-Ray Images Using Convolutional Neural Networks

According to the World Health Organization (WHO), pneumonia is a disease that causes a significant number of deaths each year. In response to this i...

vS-Graphs: Integrating Visual SLAM and Situational Graphs through Multi-level Scene Understanding

Current Visual Simultaneous Localization and Mapping (VSLAM) systems often struggle to create maps that are both semantically rich and easily interp...

Primer C-VAE: An interpretable deep learning primer design method to detect emerging virus variants

Motivation: PCR is more economical and quicker than Next Generation Sequencing for detecting target organisms, with primer design being a critical s...

A Multi-Sensor Fusion Approach for Rapid Orthoimage Generation in Large-Scale UAV Mapping

Rapid generation of large-scale orthoimages from Unmanned Aerial Vehicles (UAVs) has been a long-standing focus of research in the field of aerial m...

Learn from artificial intelligence: the pursuit of objectivity.

Humans continuously face threats from emerging novel pathogens and antimicrobial resistant bacteria or fungi, which requires urgently and efficient so...

Self-supervision via Controlled Transformation and Unpaired Self-conditioning for Low-light Image Enhancement

Real-world low-light images captured by imaging devices suffer from poor visibility and require a domain-specific enhancement to produce artifact-fr...

Using Machine Learning Techniques to Predict Viral Suppression Among People With HIV.

BACKGROUND: This study aims to develop and examine the performance of machine learning (ML) algorithms in predicting viral suppression among statewide...

Artificial Intelligence-Guided Lung Ultrasound by Nonexperts.

IMPORTANCE: Lung ultrasound (LUS) aids in the diagnosis of patients with dyspnea, including those with cardiogenic pulmonary edema, but requires techn...

An Attention-Based Deep Neural Network Model to Detect Cis-Regulatory Elements at the Single-Cell Level From Multi-Omics Data.

Cis-regulatory elements (cREs) play a crucial role in regulating gene expression and determining cell differentiation and state transitions. To captur...