NCBI PubMed Central
open access
GRMA-Net: A novel two-stage 3D Semi-supervised Pneumonia Segmentation based on Dual Multiscale Uncertainty Estimation with Graph Reasoning in Chest CTs.
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
Checking your browser - reCAPTCHA Checking your browser before accessing pmc.ncbi.nlm.nih.gov ... Click here if you are not automatically redirected after 5 seconds.
Related documents
- Convolutional Neural Network Model to Detect Coronavirus Disease 2019 (COVID-19) Pneumonia in Chest Radiographs#896267
- Beyond Boundary Noise: Aggregated Aleatoric Uncertainty Fails to Capture Presence Ambiguity in 3D Lung Nodule Segmentation#609195
- Anatomy-Aware Prediction of Bronchoscopic Accessibility from 3D CT#1141708
- Can Unsupervised Methods Outperform Supervised Deep Learning When Ground Truth Is Sparse? A Case Study of Bronchovascular Bundle Segmentation in Low-Dose CT#625403
- Can Unsupervised Methods Outperform Supervised Deep Learning When Ground Truth Is Sparse? A Case Study of Bronchovascular Bundle Segmentation in Low-Dose CT#623130
Record · ID 251306 · SHA-256 6919592f88540e58
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive