Abstract: Various methods and systems are provided for breast positioning assistance during mammography and image guided interventional procedures. In one example, a sensor detection system is utilized to evaluate one or more of a patient position, a breast position, and breast anatomy to determine if the patient and breast are adjusted to desired positions preferred for a desired view and imaging procedure prior to acquiring x-ray images, with real-time feedback provided to guide the user to position the breast and/or the patient based on the evaluation. The evaluation is performed by an artificial intelligence (AI) model stored on the x-ray mammography system that is trained using sensor data obtained from imaging procedures performed using the x-ray mammography system and fashioned into training datasets by a training module located on the x-ray mammography system. The datasets are supplied to the AI model without any transmission of the datasets exteriorly of the mammography system.

Abstract: The current disclosure provides for mapping medical images to style transferred medical images using deep neural networks, while maintaining clinical quality of the style transferred medical image, thereby enabling a clinician to evaluate medical images in a preferred style without loss of clinically relevant content. In one embodiment the current disclosure provides for a method comprising, acquiring a medical image of an anatomical region of a subject, wherein the medical image is in a first style, selecting a target style, wherein the target style is distinct from the first style, selecting a clinical quality metric, selecting a trained style transfer network based on the target style and the clinical quality metric, mapping the medical image to a style transferred medical image using the trained style transfer network, wherein the style transferred medical image is in the target style, and displaying the style transferred medical image via a display device.

Abstract: A socket with a night light function includes a housing, a connecting unit and a light emitting unit. The housing includes a base, a transmitting light member and a cover. The transmitting light member is sandwiched between the base and the cover. The connecting unit is disposed in the housing and is electrically connected to a plug. The light emitting unit includes a light emitting element disposed in the base, a photosensitive switch, a touch switch and a controller. The light emitting element, the photosensitive switch and the touch switch are all in signal connection with the controller. The photosensitive switch collaborates with the touch switch to control the operation of the light emitting element; the light intensity around the socket is increased by means of the light emitting element and the transmitting light member. The socket is convenient to use and has multiple functions.

Abstract: The current disclosure provides for mapping medical images to style transferred medical images using deep neural networks, while maintaining clinical quality of the style transferred medical image, thereby enabling a clinician to evaluate medical images in a preferred style without loss of clinically relevant content. In one embodiment the current disclosure provides for a method comprising, acquiring a medical image of an anatomical region of a subject, wherein the medical image is in a first style, selecting a target style, wherein the target style is distinct from the first style, selecting a clinical quality metric, selecting a trained style transfer network based on the target style and the clinical quality metric, mapping the medical image to a style transferred medical image using the trained style transfer network, wherein the style transferred medical image is in the target style, and displaying the style transferred medical image via a display device.

Abstract: The current disclosure provides for mapping medical images to style transferred medical images using deep neural networks, while maintaining clinical quality of the style transferred medical image, thereby enabling a clinician to evaluate medical images in a preferred style without loss of clinically relevant content. In one embodiment the current disclosure provides for a method comprising, acquiring a medical image of an anatomical region of a subject, wherein the medical image is in a first style, selecting a target style, wherein the target style is distinct from the first style, selecting a clinical quality metric, selecting a trained style transfer network based on the target style and the clinical quality metric, mapping the medical image to a style transferred medical image using the trained style transfer network, wherein the style transferred medical image is in the target style, and displaying the style transferred medical image via a display device.

Abstract: The present application relates to a resistance simulation method for a power device, comprising: establishing an equivalent resistance model of a power device, wherein the connection relationship of N fingers is equivalent to N resistors Rb connected in parallel, input ends of adjacent resistors Rb are connected by means of a resistor Ra, output ends of adjacent resistors Rb are connected by means of a resistor Rc, R a = 1 N ? R 0 , R c = 1 N ? R 1 , and Rb=RDEV*N+RS+RD, wherein R0 and R1 are respectively resistances of a source metal strip and a drain metal strip, Rs is a metal resistor of a first intermediate layer connecting one source region to the source metal strip, RD is a metal resistor of a second intermediate layer connecting one drain region to the drain metal strip, and RDEV is the channel resistance of the power device; and calculating the resistance of the equivalent resistance model as the resistance of the power device.

Abstract: The present application relates to a resistance simulation method for a power device, comprising: establishing an equivalent resistance model of a power device, wherein the connection relationship of N fingers is equivalent to N resistors Rb connected in parallel, input ends of adjacent resistors Rb are connected by means of a resistor Ra, output ends of adjacent resistors Rb are connected by means of a resistor Rc, R a = 1 N ? R 0 , R c = 1 N ? R 1 , and Rb=RDEV*N+RS+RD, wherein R0 and R1 are respectively resistances of a source metal strip and a drain metal strip, Rs is a metal resistor of a first intermediate layer connecting one source region to the source metal strip, RD is a metal resistor of a second intermediate layer connecting one drain region to the drain metal strip, and RDEV is the channel resistance of the power device; and calculating the resistance of the equivalent resistance model as the resistance of the power device.

Abstract: The current disclosure provides for mapping medical images to style transferred medical images using deep neural networks, while maintaining clinical quality of the style transferred medical image, thereby enabling a clinician to evaluate medical images in a preferred style without loss of clinically relevant content. In one embodiment the current disclosure provides for a method comprising, acquiring a medical image of an anatomical region of a subject, wherein the medical image is in a first style, selecting a target style, wherein the target style is distinct from the first style, selecting a clinical quality metric, selecting a trained style transfer network based on the target style and the clinical quality metric, mapping the medical image to a style transferred medical image using the trained style transfer network, wherein the style transferred medical image is in the target style, and displaying the style transferred medical image via a display device.

Abstract: An imaging system, such as a DBT system, is provided that is capable of providing processing of an image from initial generation/reconstruction of the image through the review of the image by a radiologist or other practitioner that produces images optimized for the particular task/process performed using the images. The task/review that the radiologist is performing on the processed or reconstructed image can be modeled with regard to the initial generation of the processed or reconstructed images from the raw images or the model considerations can be retroactively applied and back propagated through the image processing performed by the system. These modeling considerations that can take the form of an indicator of clinical performance is/are used by the system to optimize and produce an image(s) that best represents the items in the image necessary for an accurate diagnosis of the patient when reviewed by the radiologist.

Abstract: An imaging system, such as a DBT system, is provided that is capable of providing processing of an image from initial generation/reconstruction of the image through the review of the image by a radiologist or other practitioner that produces images optimized for the particular task/process performed using the images. The task/review that the radiologist is performing on the processed or reconstructed image can be modeled with regard to the initial generation of the processed or reconstructed images from the raw images or the model considerations can be retroactively applied and back propagated through the image processing performed by the system. These modeling considerations that can take the form of an indicator of clinical performance is/are used by the system to optimize and produce an image(s) that best represents the items in the image necessary for an accurate diagnosis of the patient when reviewed by the radiologist.