Abstract: A medical image assistance system and a medical image assistance method are provided. During a medical process, an image capturing apparatus located at a local location captures a first image and transmits the first image to a control device located at the local location, and the control device transmits the first image captured by the image capturing apparatus to an electronic apparatus located at a remote location. The electronic apparatus performs a marking operation on the first image to generate a second image, and transmits the second image to the control device. The control device transmits the second image to a head-mounted display apparatus located at the local location so that the head-mounted display apparatus simultaneously displays the second image and a real-time local image. The first image and the second image are at least partially the same in content.

Abstract: Aspects of the present disclosure provide an automated labeling system. For example, the automated labeling system can include an automated labeling module (ALM) configured to receive wireless signals and ground truth of learning object and label the wireless signals with the ground truth when receiving the ground truth to generate labeled training data. The automated labeling system can also include a training database coupled to the ALM. The training database can be configured to store the labeled training data.

Abstract: A method for predicting clinical severity of a neurological disorder includes steps of: a) identifying, according to a magnetic resonance imaging (MRI) image of a brain, brain image regions each of which contains a respective portion of diffusion index values of a diffusion index, which results from image processing performed on the MRI image; b) for one of the brain image regions, calculating a characteristic parameter based on the respective portion of the diffusion index values; and c) calculating a severity score that represents the clinical severity of the neurological disorder of the brain based on the characteristic parameter of the one of the brain image regions via a prediction model associated with the neurological disorder.

Abstract: A UE controls in-device coexistence (IDC) indication message generation to mitigate potential throughput impact on UE to maintain UE performance as much as possible. Under the proposed method, FDM-based solution is always preferred by the UE. Based on the IDC indication message generated by the UE, a network applies an IDC interference mitigation solution that prioritizes FDM-based solution. Specifically, the UE first sends an IDC message requesting FDM-based solution. In case the serving eNB does not respond, the UE sends a new IDC message by alternating IDC option. Even if a TDM-based solution has been received, the UE may continue requesting FDM-based solution to gain UE performance.

Abstract: A method for adaptively monitoring paging messages in a UE with a subscriber identity card camping on a cell includes: detecting a loading of a paging channel from the cell and generating a loading status; determining a monitoring pattern according to the loading status and a default monitoring pattern; and monitoring the paging channel the monitoring pattern.

Abstract: A UE controls in-device coexistence (IDC) indication message generation to mitigate potential throughput impact on UE to maintain UE performance as much as possible. Under the proposed method, FDM-based solution is always preferred by the UE. Based on the IDC indication message generated by the UE, a network applies an IDC interference mitigation solution that prioritizes FDM-based solution. Specifically, the UE first sends an IDC message requesting FDM-based solution. In case the serving eNB does not respond, the UE sends a new IDC message by alternating IDC option. Even if a TDM-based solution has been received, the UE may continue requesting FDM-based solution to gain UE performance.

Abstract: A method for adaptively monitoring paging messages in a UE with a subscriber identity card camping on a cell includes: detecting a loading of a paging channel from the cell and generating a loading status; determining a monitoring pattern according to the loading status and a default monitoring pattern; and monitoring the paging channel the monitoring pattern.