Latest AI and machine learning research in hiv/aids for healthcare professionals.
Gliomas are aggressive brain tumors that pose serious health risks. Deep learning aids in lesion segmentation, but CNN and Transformer-based models often lack context modeling or demand heavy computation, limiting real-time use on mobile medical devices. We propose GaMNet, integrating the NMamba module for global modeling and a multi-scale CNN for efficient local feature extraction. To improve i...
SUMMARY: Sustained engagement in HIV care and adherence to antiretroviral therapy (ART) are essential for achieving the UNAIDS "95-95-95" targets. Despite increased ART access, disengagement from care remains a significant issue, particularly in sub-Saharan Africa. Traditional machine learning (ML) models have shown moderate success in predicting care disengagement, which would enable early interv...
Independent learners often struggle with sustaining focus and emotional regulation in unstructured or distracting settings. Although some rely on am...
Pathologists rely on gigapixel whole-slide images (WSIs) to diagnose diseases like cancer, yet current digital pathology tools hinder diagnosis. The...
Quantitative Systems Pharmacology (QSP) promises to accelerate drug development, enable personalized medicine, and improve the predictability of cli...
Chronic diseases, including diabetes, hypertension, asthma, HIV-AIDS, epilepsy, and tuberculosis, necessitate rigorous adherence to medication to av...
Ménière's disease（MD） is a common disorder of the inner ear. The fluctuating clinical symptoms and the absence of gold standards for diagnosis have po...
Occupancy models estimate a species' occupancy probability while accounting for imperfect detection, but often overlook the issue of false-positive de...
Advances in third-generation sequencing have enabled portable and real-time genomic sequencing, but real-time data processing remains a bottleneck, ...
Age prediction from medical images or other health-related non-imaging data is an important approach to data-driven aging research, providing knowle...
Non-Intrusive Load Monitoring (NILM) has emerged as a key smart grid technology, identifying electrical device and providing detailed energy consump...
The application of artificial intelligence (AI) in medical imaging has revolutionized diagnostic practices, enabling advanced analysis and interpret...
Sequence modeling is a critical yet challenging task with wide-ranging applications, especially in time series forecasting for domains like weather ...
Despite their impressive performance, deep visual models are susceptible to transferable black-box adversarial attacks. Principally, these attacks c...
As genome sequencing is finding utility in a wide variety of domains beyond the confines of traditional medical settings, its computational pipeline...
Motivation: Viruses represent the most abundant biological entities on the planet and play vital roles in diverse ecosystems. Cataloging viruses acr...
Energy-efficient image acquisition on the edge is crucial for enabling remote sensing applications where the sensor node has weak compute capabiliti...
This study addresses the essential task of medical image segmentation, which involves the automatic identification and delineation of anatomical str...
Backscatter is an enabling technology for battery-free sensing in today's Artificial Intelligence of Things (AIOT). Building a backscatter-based sen...
Since mpox can spread from person to person, it is a zoonotic viral illness that poses a significant public health concern. It is difficult to make ...