arXiv (All)
open access
Sam-Correction: Fully Adaptive Label Noise Reduction for Medical Image Segmentation
The quality of the teacher label has a dramatic impact on the outcome of the machine learning process. Medical image segmentation often suffers from a noisy ground truth (GT) mask due to difficulties in consistent annotation. Although a recent study has proposed a promising method to curate the nois
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.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Hardware-Aware Functional Kolmogorov-Arnold Networks for Efficient Medical Image Enhancement and Segmentation#1130976
- An effective deep learning algorithm for medical image registration.#28234
- X-ray Dose Reduction Study for Cardiac Angiography and Intervention#1244506
- Interactive In-Meeting Speaker Correction with Human Feedback#1077850
- Hybrid++: The Bridge between PDE Models and Deep Learning for Gamma Noise Removal#1232931
Record · ID 1447744
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