FetUSAgents uses tool-augmented multi-agent collaboration and Dual-Path Evidence Arbitration to exceed prior MLLMs by over 25% on a new fetal ultrasound VQA benchmark.
Ultrasound in Obstetrics & Gynecology 37, 116–126
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
DACL reports up to 2.77% Dice improvement and 14.69 mm HD95 reduction in fetal US segmentation under 5% labeled data via dual-agreement consistency between CNN and Transformer networks.
citing papers explorer
-
Towards Reliable Fetal Ultrasound Interpretation with Multi-Agent Collaboration
FetUSAgents uses tool-augmented multi-agent collaboration and Dual-Path Evidence Arbitration to exceed prior MLLMs by over 25% on a new fetal ultrasound VQA benchmark.
-
Dual Agreement Consistency Learning for Semi-Supervised Fetal Ultrasound Segmentation
DACL reports up to 2.77% Dice improvement and 14.69 mm HD95 reduction in fetal US segmentation under 5% labeled data via dual-agreement consistency between CNN and Transformer networks.