An apparatus (100) comprises at least one electronic processor (101, 113) programmed to: control an associated medical imaging device (120) to acquire an image (130); compute values of textural features (132) for the acquired image; generate a signature (140) from the computed values of the textural features; and at least one of: display the signature on a display device (105); and apply an artificial intelligence (AI) component (150) to the generated signature to output image artifact metrics (152) for a set of image artifacts and display an image quality assessment based on the image artifact metrics on the display device.

Systems and methods for using quantitative ultrasound (“QUS”) techniques to generate imaging biomarkers that can be used to assess a prediction of tumor response to different chemotherapy treatment regimens are provided. For instance, the imaging biomarkers can be used to subtype tumors that have resistance to certain chemotherapy regimens prior to drug exposure. These imaging biomarkers can therefore be useful for predicting tumor response and for assessing the prognostic value of particular treatment regimens.

Systems and methods for using quantitative ultrasound (“QUS”) techniques to generate imaging biomarkers that can be used to assess a prediction of tumor response to different chemotherapy treatment regimens are provided. For instance, the imaging biomarkers can be used to subtype tumors that have resistance to certain chemotherapy regimens prior to drug exposure. These imaging biomarkers can therefore be useful for predicting tumor response and for assessing the prognostic value of particular treatment regimens.

The invention discloses an unsupervised content-preserved domain adaptation method for multiple CT lung texture recognition, which belongs to the field of image processing and computer vision. This method enables the deep network model of lung texture recognition trained in advance on one type of CT data (on the source domain), when applied to another CT image (on the target domain), under the premise of only obtaining target domain CT image and not requiring manually label the typical lung texture, the adversarial learning mechanism and the specially designed content consistency network module can be used to fine-tune the deep network model to maintain high performance in lung texture recognition on the target domain. This method not only saves development labor and time costs, but also is easy to implement and has high practicability.

Systems and methods are disclosed for estimating slipperiness of a road surface. This estimate may be obtained using an image sensor mounted on a vehicle. The estimated road slipperiness may be utilized when calculating a risk index for the road, or for an area including the road. If a predetermined threshold for slipperiness is exceeded, corrective actions may be taken. For instance, warnings may be generated to human drivers that are in control of driving vehicle, and autonomous vehicles may automatically adjust vehicle speed based upon road slipperiness detected.

Systems and methods are disclosed for estimating slipperiness of a road surface. This estimate may be obtained using an image sensor mounted on a vehicle. The estimated road slipperiness may be utilized when calculating a risk index for the road, or for an area including the road. If a predetermined threshold for slipperiness is exceeded, corrective actions may be taken. For instance, warnings may be generated to human drivers that are in control of driving vehicle, and autonomous vehicles may automatically adjust vehicle speed based upon road slipperiness detected.

Various embodiments of the present invention provide systems and method for identifying three-dimensional zone areas for use in relation to the monitoring of physical movement of a target monitor device.

Various embodiments of the present invention provide systems and method for identifying three-dimensional zone areas for use in relation to the monitoring of physical movement of a target monitor device.

Computer-implemented method for transferring style features from at least one source image to a target image, comprising the steps of generating a result image, based on the source and the target image, wherein one or more spatially-variant features of the result image correspond to one or more spatially variant features of the target image; and wherein a texture of the result image corresponds to a texture of the source image; and outputting the result image, and a corresponding device. According to the invention, the texture corresponds to a summary statistic of spatially variant features of the source image.

Computer-implemented method for transferring style features from at least one source image to a target image, comprising the steps of generating a result image, based on the source and the target image, wherein one or more spatially-variant features of the result image correspond to one or more spatially variant features of the target image; and wherein a texture of the result image corresponds to a texture of the source image; and outputting the result image, and a corresponding device. According to the invention, the texture corresponds to a summary statistic of spatially variant features of the source image.