Search:
Match:
2 results
Research Paper#Natural Language Processing, Mental Health, Semi-Supervised Learning🔬 ResearchAnalyzed: Jan 3, 2026 08:42

Uncertainty-aware Semi-supervised Ensemble for Multilingual Depression Detection

Published:Dec 31, 2025 10:35
1 min read
ArXiv

Analysis

This paper addresses the challenge of multilingual depression detection, particularly in resource-scarce scenarios. The proposed Semi-SMDNet framework leverages semi-supervised learning, ensemble methods, and uncertainty-aware pseudo-labeling to improve performance across multiple languages. The focus on handling noisy data and improving robustness is crucial for real-world applications. The use of ensemble learning and uncertainty-based filtering are key contributions.
Reference

“Tests on Arabic, Bangla, English, and Spanish datasets show that our approach consistently beats strong baselines.”

PermalinkArXiv
Research Paper#Medical Image Segmentation, Few-Shot Learning, Graph Neural Networks🔬 ResearchAnalyzed: Jan 4, 2026 00:17

Contrastive Graph Modeling for Few-Shot Medical Image Segmentation

Published:Dec 25, 2025 14:00
1 min read
ArXiv

Analysis

This paper addresses the challenge of cross-domain few-shot medical image segmentation, a critical problem in medical applications where labeled data is scarce. The proposed Contrastive Graph Modeling (C-Graph) framework offers a novel approach by leveraging structural consistency in medical images. The key innovation lies in representing image features as graphs and employing techniques like Structural Prior Graph (SPG) layers, Subgraph Matching Decoding (SMD), and Confusion-minimizing Node Contrast (CNC) loss to improve performance. The paper's significance lies in its potential to improve segmentation accuracy in scenarios with limited labeled data and across different medical imaging domains.
Reference

“The paper significantly outperforms prior CD-FSMIS approaches across multiple cross-domain benchmarks, achieving state-of-the-art performance while simultaneously preserving strong segmentation accuracy on the source domain.”

PermalinkArXiv