Abstract: Techniques for fracture detection are provided. A first image is received to be processed to identify rib fractures. A first set of regions of interest (ROIs) is identified by processing the first image using a first machine learning model, where each ROI in the first set of ROIs corresponds to a first potential fracture. Further, a first ROI of the first set of ROIs is upsampled, and the system attempts to verify the first potential fracture in the first ROI by processing the upsampled first ROI using a second machine learning model.

Abstract: Techniques for fracture detection are provided. A first image is received to be processed to identify rib fractures. A first set of regions of interest (ROIs) is identified by processing the first image using a first machine learning model, where each ROI in the first set of ROIs corresponds to a first potential fracture. Further, a first ROI of the first set of ROIs is upsampled, and the system attempts to verify the first potential fracture in the first ROI by processing the upsampled first ROI using a second machine learning model.

Abstract: A method for establishing a location anchor network includes receiving, by a location application executing on a processor, GPS positions from each of the plurality of location anchors, determining, by the location application, a preliminary network topology using the GPS positions for each of the plurality of location anchors, receiving, by the location application, an inter-location anchor spacing between the plurality of location anchors, determining, by the location application, a final network topology based on the GPS position and the inter-anchor spacing, and storing the final network topology in a memory. The plurality of location anchors are portable, and the plurality of location anchors are deployed at a location associated with a structure. The final network topology defines a position estimate for each location anchor of the plurality of location anchors.