EurekAlert!

Researchers develop multi-modal vision-language model for generalizable annotation-free pathology localization

In a study published in Nature Biomedical Engineering, a team led by Prof. WANG Shanshan from the Shenzhen Institute of Advanced Technology of the Chinese Academy of Sciences, along with Prof. ZHANG ...
IEEE

Multi-Task Contrastive Learning for Skin Lesion Classification and Segmentation

Accurate skin lesion classification and segmentation from dermoscopic images are essential for the early and effective diagnosis and management of skin cancer. This study aims to develop and evaluate ...
The Lancet

Development of a multi-task deep learning system for classification of nine common knee abnormalities on MRI: a large-scale, multicentre, stepwise validation study

aDepartment of Radiology, The Third Affiliated Hospital of Southern Medical University, Guangzhou, China bInstitute of High-End Intelligent Health Equipment, Academy of Orthopedics, Guangdong Province ...
C&EN

Deep Learning-Based Shape Classification for Hyperspectral-Imaged Microplastics

Article subjects are automatically applied from the ACS Subject Taxonomy and describe the scientific concepts and themes of the article. Figure 1 illustrates the overall workflow of the hyperspectral ...
C&EN

Optimizing Multi-Task Learning with Evolutionary Relatedness Metrics for Enhanced QSAR-Based Natural Product Activity Prediction

Article subjects are automatically applied from the ACS Subject Taxonomy and describe the scientific concepts and themes of the article. A few public databases provide biological activity data for ...
Frontiers

VAE deep learning model with domain adaptation, transfer learning and harmonization for diagnostic classification from multi-site neuroimaging data

In large public multi-site fMRI datasets, the sample characteristics, data acquisition methods, and MRI scanner models vary across sites and datasets. This non-neural variability obscures neural ...
IEEE

Distributed Multi-Task In-Network Classification on Programmable Switches by Ensemble Models

Abstract: Offloading machine learning models for network classification on high-throughput programmable switches is a promising technology, enabling line-speed in-network classification. Existing ...
Frontiers

Echocardiographic video-driven multi-task learning model for coronary artery disease diagnosis and severity grading

Introduction: Echocardiography is a first-line noninvasive test for diagnosing coronary artery disease (CAD), but it depends on time-consuming visual assessments by experts. Methods: This study ...