arXiv (All)
open access
Layer-Wise Gate-Controlled Prompt Truncation in a Multimodal Chest X-Ray Classifier
Mixture of Prompt Experts (MoPE) adapts multimodal transformers through input-dependent prompt composition, while retaining a fixed prompt length. We investigate a layer-wise gating extension in a binary chest X-ray classification pilot study. The controller predicts a retention ratio for each sampl
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
- Subgroup performance analysis of adaptation strategies for chest X-ray foundation models#926179
- CALM-CXR: a calibration-aware lung-masked workflow for hierarchical chest X-ray classification.#452822
- Does Vision-Language Pretraining Granularity Matter? A Controlled Evaluation of Vision-Language Objectives Across Chest X-Ray Interpretation Tasks#1115941
- CORTEX: A Structured Reasoning Benchmark for Trustworthy 3D Chest CT MLLMs#1062944
- Vision-Language Generative Model for View-Specific Chest X-ray Generation#442734
Record · ID 974699
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