Spiral
metadata only
Detecting outliers with poisson image interpolation
artificial-intelligencebiomedicalbiomedicinecomputer-scienceengineeringmedical-informaticssoftware-engineering
Supervised learning of every possible pathology is unrealistic for many primary care applications like health screening. Image anomaly detection methods that learn normal appearance from only healthy data have shown promising results recently. We propose an alternative to image reconstruction-based
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
- Deep Pseudo-Proximal Map: A Self-Supervised Data-Fitting Agent for Iterative Reconstruction#1105286
- Decoupled Data Consistency with Diffusion Purification for Image Restoration#821650
- What Remains Normal? Clean Images Miss Useful Near-Defect Normal Patches for Anomaly Detection#1035596
- Conditioning noise is a free regularizer for LoRA fine-tuning: no pathology encoder required for diffusion-based artifact detection in histopathology#1110428
- Anomaly Detection and Generation with Diffusion Models: A Survey#1199671
Record · ID 1435797
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