首页|Reports from Technical University Munich (TU Munich) Advance Knowledge in Robotics (Intelligent robotic sonographer: Mutual information-based disentangled reward learning from few demon- strations)
Reports from Technical University Munich (TU Munich) Advance Knowledge in Robotics (Intelligent robotic sonographer: Mutual information-based disentangled reward learning from few demon- strations)
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2024 FEB 02 (NewsRx) – By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on robotics. According to news reporting originating from Munich, Germany, by NewsRx correspondents, research stated, “Ultrasound (US) imaging is widely used for biometric measurement and diagnosis of internal organs due to the advantages of being real- time and radiation-free. However, due to inter-operator variations, resulting images highly depend on the experience of sonographers.” Our news correspondents obtained a quote from the research from Technical University Munich (TU Mu- nich): “This work proposes an intelligent robotic sonographer to autonomously “explore” target anatomies and navigate a US probe to standard planes by learning from the expert. The underlying high-level phys- iological knowledge from experts is inferred by a neural reward function, using a ranked pairwise image comparison approach in a self-supervised fashion. This process can be referred to as understanding the “language of sonography.” Considering the generalization capability to overcome inter-patient variations, mutual information is estimated by a network to explicitly disentangle the task-related and domain features in latent space. The robotic localization is carried out in coarse-to-fine mode based on the predicted reward associated with B-mode images. To validate the effectiveness of the proposed reward inference network, representative experiments were performed on vascular phantoms (“line” target), two types of ex vivo animal organ phantoms (chicken heart and lamb kidney representing “point” target), and in vivo human carotids. To further validate the performance of the autonomous acquisition framework, physical robotic acquisitions were performed on three phantoms (vascular, chicken heart, and lamb kidney).”
Technical University Munich (TU Munich)MunichGermanyEuropeEmerging TechnologiesMachine LearningRoboticsRobots
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Sage
