LoGSAM: Parameter-Efficient Cross-Modal Grounding for MRI Segmentation
LoGSAM: Parameter-Efficient Cross-Modal Grounding for MRI Segmentation explores LoGSAM is a parameter-efficient framework for MRI tumor segmentation using radiologist dictation as prompts.. Commercial viability score: 3/10 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
- Higher ViabilityPaper Title: LoV3D: Grounding Cognitive Prognosis Reasoning in Longitudinal 3D Brain MRI via Regional Volume AssessmentsViability: 8.0
- Higher ViabilityMedical Image Spatial Grounding with Semantic SamplingViability: 7.0
- Higher Viability3D Modality-Aware Pre-training for Vision-Language Model in MRI Multi-organ Abnormality DetectionViability: 5.0
- Higher ViabilityMedSAM-Agent: Empowering Interactive Medical Image Segmentation with Multi-turn Agentic Reinforcement LearningViability: 7.0
- Higher Viability