HIV/AIDS

Dc-EEMF: Pushing depth-of-field limit of photoacoustic microscopy via decision-level constrained learning

Photoacoustic microscopy holds the potential to measure biomarkers' structural and functional status without labels, which significantly aids in comprehending pathophysiological conditions in biomedical research. However, conventional optical-resolution photoacoustic microscopy (OR-PAM) is hindered by a limited depth-of-field (DoF) due to the narrow depth range focused on a Gaussian beam. Conseq...

Scalable and robust machine learning framework for HIV classification using clinical and laboratory data.

Human Immunodeficiency Virus (HIV) is a retrovirus that weakens the immune system, increasing vulnerability to infections and cancers. HIV spreads primarily via sharing needles, from mother to child during childbirth or breastfeeding, or unprotected sexual intercourse. Therefore, early diagnosis and treatment are crucial to prevent the disease progression of HIV to AIDS, which is associated with h...

A Framework for Adversarial Analysis of Decision Support Systems Prior to Deployment

This paper introduces a comprehensive framework designed to analyze and secure decision-support systems trained with Deep Reinforcement Learning (DR...

FeatInv: Spatially resolved mapping from feature space to input space using conditional diffusion models

Internal representations are crucial for understanding deep neural networks, such as their properties and reasoning patterns, but remain difficult t...

InstGenIE: Generative Image Editing Made Efficient with Mask-aware Caching and Scheduling

Generative image editing using diffusion models has become a prevalent application in today's AI cloud services. In production environments, image e...

PreP-OCR: A Complete Pipeline for Document Image Restoration and Enhanced OCR Accuracy

This paper introduces PreP-OCR, a two-stage pipeline that combines document image restoration with semantic-aware post-OCR correction to enhance bot...

Reward-Driven Interaction: Enhancing Proactive Dialogue Agents through User Satisfaction Prediction

Reward-driven proactive dialogue agents require precise estimation of user satisfaction as an intrinsic reward signal to determine optimal interacti...

A Coarse to Fine 3D LiDAR Localization with Deep Local Features for Long Term Robot Navigation in Large Environments

The location of a robot is a key aspect in the field of mobile robotics. This problem is particularly complex when the initial pose of the robot is ...

Prospective study using artificial neural networks for identification of high-risk COVID-19 patients.

The COVID-19 pandemic caused a major public health crisis, with severe impacts on global health and the economy. Machine learning (ML) has been crucia...

Predicting and interpreting key features of refractory Mycoplasma pneumoniae pneumonia using multiple machine learning methods.

In recent years, the incidence of refractory Mycoplasma pneumoniae pneumonia (RMPP) has significantly risen, posing severe pulmonary and extrapulmonar...

Self-GIVE: Associative Thinking from Limited Structured Knowledge for Enhanced Large Language Model Reasoning

When addressing complex questions that require new information, people often associate the question with existing knowledge to derive a sensible ans...

FasNet: a hybrid deep learning model with attention mechanisms and uncertainty estimation for liver tumor segmentation on LiTS17.

Liver cancer, especially hepatocellular carcinoma (HCC), remains one of the most fatal cancers globally, emphasizing the critical need for accurate tu...

HydraInfer: Hybrid Disaggregated Scheduling for Multimodal Large Language Model Serving

Multimodal Large Language Models (MLLMs) have been rapidly advancing, enabling cross-modal understanding and generation, and propelling artificial i...

Design of multifunctional tunable dual-layer metalens based on deep learning.

To address the challenges of complex control in individually tunable meta-atoms and the limited modulation performance of single-layer holistic tunabl...

Development of a machine learning prediction model for loss to follow-up in HIV care using routine electronic medical records in a low-resource setting.

BACKGROUND: Despite the global commitment to ending AIDS by 2030, the loss of follow-up (LTFU) in HIV care remains a significant challenge. To address...

A computational system to handle the orthographic layer of tajwid in contemporary Quranic Orthography

Contemporary Quranic Orthography (CQO) relies on a precise system of phonetic notation that can be traced back to the early stages of Islam, when th...

HSRMamba: Efficient Wavelet Stripe State Space Model for Hyperspectral Image Super-Resolution

Single hyperspectral image super-resolution (SHSR) aims to restore high-resolution images from low-resolution hyperspectral images. Recently, the Vi...

GNN-Suite: a Graph Neural Network Benchmarking Framework for Biomedical Informatics

We present GNN-Suite, a robust modular framework for constructing and benchmarking Graph Neural Network (GNN) architectures in computational biology...

Automated Caries Detection Under Dental Restorations and Braces Using Deep Learning.

In the dentistry field, dental caries is a common issue affecting all age groups. The presence of dental braces and dental restoration makes the detec...

Evaluating machine learning algorithms for predicting HIV status among young Thai men who have sex with men.

OBJECTIVE: This study aimed to develop machine learning (ML) models to predict HIV status and assessed the factors associated with HIV infection among...