IL dyslexia screening takes effect Jan. 1, drawing reading instruction debate

As Illinois rolls out a new law requiring early literacy screenings beginning Jan. 1, some educators question whether it will ...
pharmaphorum

The patient segmentation model

Dividing patients into groups based on how they behave towards their condition can aid understanding of the issues that affect them and improve outcomes, such as quality of life in long-term ...
Investopedia

Master Market Segmentation for Enhanced Profitability and Growth

Ann Behan has 10 years-plus of experience researching, writing, and editing articles, white papers, and executing searches at the board level across various industries. Her expertise includes ...
Scientific Research Publishing

Early Alzheimer’s Disease Detection from Short Speech Samples Using Lightweight, Interpretable Linguistic Markers ()

Based Detection, Linguistic Biomarkers, Machine Learning, Explainable AI, Cognitive Decline Monitoring Share and Cite: de Filippis, R. and Al Foysal, A. (2025) Early Alzheimer’s Disease Detection from ...
IEEE

MTANet: Multi-Task Attention Network for Automatic Medical Image Segmentation and Classification

Abstract: Medical image segmentation and classification are two of the most key steps in computer-aided clinical diagnosis. The region of interest were usually segmented in a proper manner to extract ...
GitHub

XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model

New VOS project: Putting the Object Back into Video Object Segmentation: https://github.com/hkchengrex/Cutie We frame Video Object Segmentation (VOS), first and ...
IEEE

A Lightweight Semantic Segmentation Network Based on Self-Attention Mechanism and State Space Model for Efficient Urban Scene Segmentation

Abstract: In the semantic segmentation of remote sensing images, methods based on convolutional neural networks (CNNs) and Transformers have been extensively studied. Nevertheless, CNN struggles to ...
GitHub

UniverSeg: Universal Medical Image Segmentation

Given a new segmentation task (e.g. new biomedical domain, new image type, new region of interest, etc), most existing strategies involve training or fine-tuning a segmentation model that takes an ...