arXiv (All)
open access
Spectral Adapters for Segment Anything Model-based Segmentation of Colorectal Liver Metastases in Computed Tomography
Accurate segmentation of colorectal liver metastases (CRLM) in contrast-enhanced computed tomography (CT) is important for response assessment, surgical planning, and follow-up. We propose two parameter-efficient spectral adapters for the Segment Anything Model (SAM): the Directional Spectral Adapte
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
- Cross-Spectral Dense Correspondence for Multimodal Spectral Medical Imaging#792353
- Cross-Spectral Dense Correspondence for Multimodal Spectral Medical Imaging#788353
- Development of a nomogram for predicting extramural venous invasion in colon cancer using spectral CT multiparameter imaging.#975841
- Learning Spectral Allocation: A Fractional Diffusion Framework for Adaptive Volumetric Segmentation#1063428
- Spectral Precise Image-Colon Cancer#912409
Record · ID 999394
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