Devdiscourse

AI unlocks hidden signals in drug reviews to flag safety concerns faster

The study, titled “GenAI-Powered Framework for Reliable Sentiment Labeling in Drug Safety Monitoring,” published in Applied ...
eLife

New idtracker.ai rethinks multi-animal tracking as a representation learning problem to increase accuracy and reduce tracking time

Identification of each animal in a collective becomes possible even when individuals are never all visible simultaneously, enabling faster and more accurate analysis of collective behavior.
IEEE

Multi-Label Text Classification Using Prompting Techniques via LLMs in Software Effort Estimation

Abstract: Text classification (TC) has gained substantial importance across diverse domains due to the increasing proliferation of internet-based and digital technologies. Organizations worldwide ...
Frontiers

Disease classification via interpretable machine learning based on multi-center routine coagulation test

This study aims to establish an interpretable disease classification model via machine learning and identify key features related to the disease to assist clinical disease diagnosis based on a ...
IEEE

Multi-Label Classification of Complex Power Quality Disturbances via Label-Semantics Fusion and Transformer Encoder

Abstract: The increasing penetration of renewable energy sources and the widespread use of power electronic devices have introduced complex power quality disturbances (PQDs), which are essentially ...
GitHub

Progressive Uncertainty-Aware Pretraining for Medical X-ray Classification

A novel deep learning framework that combines ImageNet pretraining with uncertainty-aware curriculum learning for robust multi-label chest X-ray classification. The key innovation is an adaptive ...
Microsoft

Multi-Label Classification for Colon Cancer Using Histopathological Images

Colon cancer classification has a significant guidance value in clinical diagnoses and medical prognoses. The classification of colon cancers with high accuracy is the premise of efficient treatment.
C&EN

DART Predictor: A Multi-Label Attention Model for High-Throughput Screening of Chemicals with Developmental and Reproductive Toxicity (DART)

State Key Laboratory of Soil Pollution Control and Safety, and Women’s Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China ...
Frontiers

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

The acquisition sites include: CALTECH, California Institute of Technology; CMU, Carnegie Mellon University; KKI, Kennedy Krieger Institute; LEUVEN, University of Leuven; MAX, Ludwig Maximilians ...