Abstract: An image processing method is provided that automatically calculates Body Surface Area (BSA) score using machine learning techniques. A Felzenszwalb image segmentation algorithm is used to define proposed regions in each of a plurality of training set images. The training set images are oversegmented, and then each of the proposed regions in each of the plurality of oversegmented training set images are manually classified as being a lesion or a non-lesion. A Convolutional Neural Network (CNN) is then trained using the manually classified proposed regions in each of the plurality of training set images. The trained CNN is then used on test images to calculate BSA scores.

Abstract: An image processing method is provided that automatically calculates Body Surface Area (BSA) score using machine learning techniques. A Felzenszwalb image segmentation algorithm is used to define proposed regions in each of a plurality of training set images. The training set images are oversegmented, and then each of the proposed regions in each of the plurality of oversegmented training set images are manually classified as being a lesion or a non-lesion. A Convolutional Neural Network (CNN) is then trained using the manually classified proposed regions in each of the plurality of training set images. The trained CNN is then used on test images to calculate BSA scores.

Abstract: An image processing method is provided that automatically calculates Body Surface Area (BSA) score using machine learning techniques. A Felzenszwalb image segmentation algorithm is used to define proposed regions in each of a plurality of training set images. The training set images are oversegmented, and then each of the proposed regions in each of the plurality of oversegmented training set images are manually classified as being a lesion or a non-lesion. A Convolutional Neural Network (CNN) is then trained using the manually classified proposed regions in each of the plurality of training set images. The trained CNN is then used on test images to calculate BSA scores.

Abstract: An image processing method is provided that automatically calculates Body Surface Area (BSA) score using machine learning techniques. A Felzenszwalb image segmentation algorithm is used to define proposed regions in each of a plurality of training set images. The training set images are oversegmented, and then each of the proposed regions in each of the plurality of oversegmented training set images are manually classified as being a lesion or a non-lesion. A Convolutional Neural Network (CNN) is then trained using the manually classified proposed regions in each of the plurality of training set images. The trained CNN is then used on test images to calculate BSA scores.

Abstract: The present invention relates to xanthone analogs. Such compounds may be used in the treatment of bacterial infections.

Abstract: An electronic system includes a control unit configured to generate a firmware register and store a system health information to the firmware register; and a communication unit, coupled to the control unit, configured to retrieve at least a portion of the system health information and communicate the portion of the system health information in response to a singular read request independent of an operational status of a host device.

Abstract: An electronic system includes a control unit configured to generate a firmware register and store a system health information to the firmware register; and a communication unit, coupled to the control unit, configured to retrieve at least a portion of the system health information and communicate the portion of the system health information in response to a singular read request independent of an operational status of a host device.

Abstract: The present invention relates to xanthone analogs. Such compounds may be used in the treatment of bacterial infections.

Abstract: An apparatus and method for packing a data bus by moving ports that are associated with assigned time slots from a first set of time slots to a second set of time slots on the data bus. After all the time slots that can be moved are moved, the remaining time slots are shifted on the data bus resulting in larger contiguous groupings of time slots.