WeNLEX: Weakly Supervised Natural Language Explanations for Multilabel Chest X-ray Classification
WeNLEX: Weakly Supervised Natural Language Explanations for Multilabel Chest X-ray Classification explores A weakly supervised system that generates faithful and plausible natural language explanations for chest X-ray classifications, improving diagnostic accuracy and adapting to different audiences.. Commercial viability score: 7/10 in Medical AI.
BUILDER'S SANDBOX
Build This Paper
Use an AI coding agent to implement this research.
OpenAI CodexAI Agent
Lightweight coding agent in your terminal.
Claude CodeAI Agent
Agentic coding tool for terminal workflows.
AntiGravity IDEScaffolding
AI agent mindset installer and workflow scaffolder.
CursorIDE
AI-first code editor built on VS Code.
VS CodeIDE
Free, open-source editor by Microsoft.
Recommended Stack
Startup Essentials
Estimated $9K - $13K over 6-10 weeks.
See exactly what it costs to build this -- with 3 comparable funded startups.
7-day free trial. Cancel anytime.
Discover the researchers behind this paper and find similar experts.
7-day free trial. Cancel anytime.
Related Papers
- Builds On ThisPONTE: Personalized Orchestration for Natural Language Trustworthy ExplanationsViability: 5.0
- Builds On ThisGNN Explanations that do not Explain and How to find ThemViability: 5.0
- Builds On ThisWhy Does It Look There? Structured Explanations for Image ClassificationViability: 5.0
- Builds On ThisNEX: Neuron Explore-Exploit Scoring for Label-Free Chain-of-Thought Selection and Model RankingViability: 6.0
- Prior WorkMLLM-based Textual Explanations for Face ComparisonViability: 7.0