arXiv (All)
open access
Thyroid Nodule Segmentation in Ultrasound Images with Conformal Uncertainty Estimation
Thyroid nodules affect a large proportion of the general population. Although most are benign, a non-negligible fraction is malignant. Ultrasound imaging is the primary diagnostic modality, and accurate nodule segmentation is a prerequisite for CAD systems to classify malignancy and assess metastati
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
- Integration of AI-driven ultrasound and molecular testing to improve diagnosis of indeterminate thyroid nodules.#1284721
- Thyroidiomics: An Automated Pipeline for Segmentation and Classification of Thyroid Pathologies from Scintigraphy Images#625456
- Thyroidiomics: An Automated Pipeline for Segmentation and Classification of Thyroid Pathologies from Scintigraphy Images#623183
- An online nomogram based on bimodal ultrasound images for preoperative diagnosis of cytologically indeterminate thyroid nodules.#368011
- Knowledge-augmented deep multi-task learning for nodule segmentation and classification in thyroid ultrasound images#1028255
Record · ID 1297956
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