NVIDIA Base Command
2 use cases using this technology
Accelerating the Radiological Workflow With AI at University of Wisconsin–Madison
University of Wisconsin–Madison Department of Radiology
The UW-Madison Department of Radiology uses NVIDIA DGX BasePOD with the MONAI imaging framework (integrated into Flywheel's healthcare data platform) and NVIDIA FLARE for federated learning to curate imaging datasets and rapidly iterate on AI models for tasks like pediatric bone age assessment and opportunistic screening. Ten thousand abdominal CT cases that previously took six to eight months to process manually can now be processed in a day; over one million images can be processed in under a day. The tools are being deployed via containers to a 21-site global clinical trial.
BMW Group boosts production efficiency with NVIDIA DGX and generative AI
BMW Group
BMW Group uses NVIDIA DGX systems to train deep-learning-based synthetic data generation models that power SORDI (Synthetic Object Recognition Dataset for Industries), the largest open-source dataset for industrial AI with over 800,000 photorealistic images across 80 categories. DGX systems deliver an 8x boost in data scientist productivity and 4-6x improved performance over BMW's prior legacy systems; AI training for door-sill defect detection now takes under an hour using as few as five images, and the time required for employees to implement AI automation in QA tasks overall has been cut by more than two-thirds.