Abstract: The present disclosure relates to a device and a system for testing flatness. The device for testing flatness includes a base, a testing platform, and a ranging sensor. The testing platform is assembled on the base. The testing platform includes a supporting structure. The supporting structure is disposed on the side of the testing platform away from the base and is used to support a to-be-tested board. The structure matches the structure of the to-be-tested board. The ranging sensor is disposed on the side of the testing platform away from the base. After the to-be-tested board is placed on the testing platform, the ranging sensor is used to test distances between a number N of to-be-tested positions on the to-be-tested board and the ranging sensor, to obtain N pieces of distance information, and the N pieces of distance information are used to determine the flatness of the to-be-tested board, where N is an integer greater than 2.

Abstract: Embodiments of the present disclosure include a method, device and computer readable medium involving receiving image data to detect tissue lesions, passing the image data through at least one first convoluted neural network, segmenting the image data, fusing the segmented image data, and detecting tissue lesions.

Abstract: Embodiments of the disclosure provide systems and methods for generating a diagnosis report based on a medical image of a patient. The system includes a communication interface configured to receive the medical image acquired by an image acquisition device. The system further includes at least one processor. The at least one processor is configured to detect a medical condition based on the medical image and automatically generate text information describing the medical condition. The at least one processor is further configured to construct the diagnosis report, where the diagnosis report includes at least one image view showing the medical condition and a report view including the text information describing the medical condition. The system also includes a display configured to display the diagnosis report.

Abstract: Embodiments of the disclosure provide systems and methods for segmenting an image. An exemplary system includes a communication interface configured to receive the image acquired by an image acquisition device. The system further includes a memory configured to store a multi-level learning network comprising a plurality of convolution blocks cascaded at multiple levels. The system also includes a processor configured to apply a first convolution block and a second convolution block of the multi-level learning network to the image in series. The first convolution block is applied to the image and the second convolution block is applied to a first output of the first convolution block. The processor is further configured to concatenate the first output of the first convolution block and a second output of the second convolution block to obtain a feature map and obtain a segmented image based on the feature map.

Abstract: Embodiments of the disclosure provide systems and methods for generating a report based on a medical image of a patient. An exemplary system includes a communication interface configured to receive the medical image acquired by an image acquisition device. The system may further include at least one processor. The at least one processor is configured to automatically determine keywords from a natural language description of the medical image generated by applying a learning network to the medical image. The at least one processor is further configured to generate the report describing the medical image of the patient based on the keywords. The at least one processor is also configured to provide the report for display.

Abstract: Embodiments of the present disclosure include a method, device and computer readable medium involving receiving image data to detect tissue lesions, passing the image data through at least one first convoluted neural network, segmenting the image data, fusing the segmented image data, and detecting tissue lesions.

Abstract: A medical image processing method and apparatus, and an image processing method and apparatus, terminal and storage medium that obtains a to-be-processed medical image; generates a difference image according to the first image data, the second image data, and the third image data included in the to-be-processed medical image; and performs binarization processing on the difference image to obtain a binarized image, a foreground region of the binarized image corresponding to a pathological tissue region of the to-be-processed medical image. A difference image is generated based on color information of different channels before binarization processing is performed on an image, thereby effectively using the color information in the image. The pathological tissue region extracted based on the difference image is more accurate and facilitates subsequent image analysis.

Abstract: Embodiments of the present disclosure include a method, device and computer readable medium involving receiving image data to detect tissue lesions, passing the image data through at least one first convoluted neural network, segmenting the image data, fusing the segmented image data, and detecting tissue lesions.

Abstract: The present disclosure is directed to a method and system for automatically detecting a physiological condition from a medical image of a patient. The method may include receiving the medical image acquired by an imaging device. The method may further include detecting, by a processor, target objects and obtaining the corresponding target object patches from the received medical image. And the method may further include determining, by the processor, a first parameter using a first learning network for each target object patch. The first parameter represents the physiological condition level of the corresponding target object, and the first learning network is trained by adding one or more auxiliary classification layers. This method can quickly, accurately, and automatically predict target object level and/or image (patient) level physiological condition from a medical image of a patient by means of a learning network, such as 3D learning network.

Abstract: A method of displaying an augmented reality (AR) image includes controlling an AR display to display the AR image as being at least partially overlaid over a view through a microscope while the view is visible to a user of the microscope, wherein the AR image includes a cursor; receiving a user input from a user interface; setting an anchor point based on the user input; updating the AR image to include the anchor point; controlling the AR display to display the updated AR image; detecting a motion of a slide of the microscope; adjusting the AR image by moving at least one from among the cursor and the anchor point within the AR image based on the detected motion; and controlling the AR display to display the adjusted AR image.

Abstract: Embodiments of the disclosure provide systems and methods for generating a diagnosis report based on a medical image of a patient. The system includes a communication interface configured to receive the medical image acquired by an image acquisition device. The system further includes at least one processor. The at least one processor is configured to detect a medical condition based on the medical image and automatically generate text information describing the medical condition. The at least one processor is further configured to construct the diagnosis report, where the diagnosis report includes at least one image view showing the medical condition and a report view including the text information describing the medical condition. The system also includes a display configured to display the diagnosis report.

Abstract: A method and apparatus include performing a first image analysis at a first resolution of an input tissue image. An uncertainty map is generated based on performing the first image analysis. A set of uncertain regions of the input tissue image are identified based on the uncertainty map. A second image analysis of the set of uncertain regions is performed at a second resolution of the tissue image that is greater than the first resolution. An analysis result is generated based on the first image analysis and the second image analysis.

Abstract: Embodiments of the disclosure provide systems and methods for generating a diagnosis report based on a medical image of a patient. The system includes a communication interface configured to receive the medical image acquired by an image acquisition device. The system further includes at least one processor. The at least one processor is configured to detect a medical condition based on the medical image and automatically generate text information describing the medical condition. The at least one processor is further configured to construct the diagnosis report, where the diagnosis report includes at least one image view showing the medical condition and a report view including the text information describing the medical condition. The system also includes a display configured to display the diagnosis report.

Abstract: A method, device, system, and computer-readable medium for displaying an augmented reality (AR) image, including controlling a camera to capture an image of a view through a microscope; generating information corresponding to the image; generating the AR image including an information ring configured to display the information; and controlling an AR display to display the AR image such that the information ring is at least partially overlaid over a periphery of the view through the microscope while the view is visible to a user of the microscope.

Abstract: A method, device, system, and computer-readable medium for displaying an augmented reality (AR) image, including controlling a camera to capture an image of a view through a microscope; generating information corresponding to the image; generating the AR image including an information ring configured to display the information; and controlling an AR display to display the AR image such that the information ring is at least partially overlaid over a periphery of the view through the microscope while the view is visible to a user of the microscope.

Abstract: Embodiments of the present disclosure include a method, device and computer readable medium involving receiving image data to detect tissue lesions, passing the image data through at least one first convoluted neural network, segmenting the image data, fusing the segmented image data, and detecting tissue lesions.

Abstract: A method and apparatus include performing a first image analysis at a first resolution of an input tissue image. An uncertainty map is generated based on performing the first image analysis. A set of uncertain regions of the input tissue image are identified based on the uncertainty map. A second image analysis of the set of uncertain regions is performed at a second resolution of the tissue image that is greater than the first resolution. An analysis result is generated based on the first image analysis and the second image analysis.

Abstract: A method of displaying an augmented reality (AR) image includes controlling an AR display to display the AR image as being at least partially overlaid over a view through a microscope while the view is visible to a user of the microscope, wherein the AR image includes a cursor; receiving a user input from a user interface; setting an anchor point based on the user input; updating the AR image to include the anchor point; controlling the AR display to display the updated AR image; detecting a motion of a slide of the microscope; adjusting the AR image by moving at least one from among the cursor and the anchor point within the AR image based on the detected motion; and controlling the AR display to display the adjusted AR image.

Abstract: Embodiments of the disclosure provide systems and methods for generating a report based on a medical image of a patient. An exemplary system includes a communication interface configured to receive the medical image acquired by an image acquisition device. The system may further include at least one processor. The at least one processor is configured to automatically determine keywords from a natural language description of the medical image generated by applying a learning network to the medical image. The at least one processor is further configured to generate the report describing the medical image of the patient based on the keywords. The at least one processor is also configured to provide the report for display.

Abstract: Embodiments of the disclosure provide systems and methods for generating a diagnosis report based on a medical image of a patient. The system includes a communication interface configured to receive the medical image acquired by an image acquisition device. The system further includes at least one processor. The at least one processor is configured to detect a medical condition based on the medical image and automatically generate text information describing the medical condition. The at least one processor is further configured to construct the diagnosis report, where the diagnosis report includes at least one image view showing the medical condition and a report view including the text information describing the medical condition. The system also includes a display configured to display the diagnosis report.