GE HealthCare Accelerates AI Innovation with Healthcare-Specific Foundation Models Powered by NVIDIA


GE HealthCare is using NVIDIA technology to develop its recent research model, SonoSAMTrack, which combines a promptable foundation model for segmenting objects on ultrasound images called SonoSAM. SonoSAMTrack focuses on segmenting anatomies, lesions, and other essential areas in ultrasound images. SonoSAMLite is a streamlined version of SonoSAMTrack.

Due to their ability to operate as human-in-the-loop AI systems, foundation and generative AI models play a crucial role by enabling swift adaptation to various diseases, facilitating screening, early detection, tracking progression, and identifying non-invasive biomarkers with minimal training requirements. In a recent study conducted by GE HealthCare, its research project, SonoSAMTrack, showcased high performance across seven ultrasound datasets, covering a wide range of anatomies (adult heart and fetal head) and pathologies (breast lesions and musculoskeletal pathologies), as well as different scanning devices. In particular, it significantly outperformed competing methods. In addition, SonoSamTrack demonstrated improved performance metrics in terms of speed and efficiency, requiring only 2-6 clicks for accurate segmentation, minimising user input. This achievement was made possible by distillation and quantization techniques using the NVIDIA TensorRT software development kit and other quantization-aware training capabilities.

By utilizing these versatile, generalist models, we aim to adapt more efficiently to new tasks and medical imaging modalities, often requiring far less labeled data compared to the traditional model retraining approach,” said Parminder Bhatia, Chief AI Officer of GE HealthCare.

The combination of NVIDIA’s accelerated computing and AI technology stack with GE HealthCare’s medical imaging expertise will help enhance patient care by making ultrasound diagnostics quicker and more accurate,” said David Niewolny, Director of Business Development for Healthcare and Medical at NVIDIA.

To view the source version, please click HERE.

en_GBEnglish (UK)