arXiv (All)
open access
SPARCL: Spatially Adaptive Regional Coverage for Active Learning in Chest X-ray Analysis
Pixel-level annotation for chest X-ray (CXR) lesion segmentation is expensive, making active learning (AL) essential. However, existing AL strategies fail to account for the spatial distribution of selected samples, resulting in inconsistent performance across focal (e.g., aortic calcification) and
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
- Projection-Related Bias in the Detection of Thoracic Abnormalities: A Large-Scale Analysis of the NIH ChestX-Ray14 Dataset.#232349
- CALM-CXR: a calibration-aware lung-masked workflow for hierarchical chest X-ray classification.#452822
- XFlow: A Workflow Model for Instruction-Guided Lesion Segmentation in Chest X-rays#1128780
- Federated LoRA Adaptation of BiomedCLIP Across Four International Chest X-Ray Cohorts#822503
- Anatomy-Grounded Weakly Supervised Prompt Tuning for Chest X-ray Latent Diffusion Models#987443
Record · ID 1343015
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