Abstract: A medical image processing method is provided to a data processing device. The method includes obtaining a medical image, obtaining a feature map and a medical image content recognition result that correspond to the medical image by using a medical image classification model, or obtaining the feature map and a lesion classification result that correspond to the medical image by using the medical classification model, the feature map including N channels, N being an integer greater than 1, generating a thermodynamic diagram corresponding to the medical image content recognition result or the lesion classification result according to the feature map and a weight parameter set, the weight parameter set including N weight parameters, the weight parameters having a correspondence with the channels, and generating an image recognition result corresponding to the medical image according to the thermodynamic diagram.

Abstract: The present disclosure relates to a method for efficiently compressing a dynamic 3D model sequence based on 4D fusion. In some embodiments, comprising: storing a 3D model of one frame of the dynamic 3D model sequence as a reference model; determining an initial correspondence between vertices of the reference model and a target model to align the reference model and the target model by optimizing an energy function of the reference model so as to obtain initialized fusion parameters, wherein the target model is a 3D model of remaining frames of the dynamic 3D model sequence; and determining the final fusion parameters for deforming the reference model into the target model by iteratively optimizing the initialized fusion parameters.

Abstract: A major immune dysfunction persisting in chronic diseases such as HIV infection and cancer is the impairment of T cell motility and migration to tissues. Therapeutics such as antiretroviral therapy or cancer therapy frequently do not fully restore T cell motility for tissue migration and infiltration. Cofilin is an actin-depolymerizing factor that regulates actin dynamics for T cell migration. Here we demonstrate that the levels of cofilin phosphorylation in blood T cells (CD4 or CD8), macrophages, B cells, natural killer (NK) cells, and/or cancer cells can be used to quantify the immune damages caused by chronic viral infection and cancer, and to predict the recovery of T cells following treatment.

Abstract: A power-supply mode switching circuit and an aerosol generating device are provided. The power-supply mode switching circuit includes an external-power-source power-supply circuit, a protection circuit, and a battery power-supply circuit. A node where the external-power-source power-supply circuit is connected to the protection circuit serves as a power-source input end. A node where the external-power-source power-supply circuit is connected to the battery power-supply circuit serves as a power supply end. The protection circuit is configured to control the battery power-supply circuit to be cut off when the power-source input end is connected to the external power source, to make the external power source supply power to the power supply end. The protection circuit is further configured to control the battery power-supply circuit to be conducted when the power-source input end is not connected to the external power source, to make the battery supply power to the power supply end.

Abstract: An artificial intelligence-based image processing method includes: obtaining a first sample image of a source domain and a second sample image of a target domain, the first sample image of the source domain carrying a corresponding target processing result; converting the first sample image into a target sample image, the target sample image carrying a corresponding target processing result; training a first image processing model based on the target sample image and the target processing result corresponding to the target sample image, to obtain a second image processing model; and inputting, in response to obtaining a human tissue image of the target domain, the human tissue image into the second image processing model, positioning, by the second image processing model, a target human tissue in the human tissue image, and outputting position information of the target human tissue in the human tissue image.

Abstract: A method for training an image recognition model includes: obtaining training image sets; obtaining a first predicted probability, a second predicted probability, a third predicted probability, and a fourth predicted probability based on the training image sets by using an initial image recognition model; determining a target loss function according to the first predicted probability, the second predicted probability, the third predicted probability, and the fourth predicted probability; and training the initial image recognition model based on the target loss function, to obtain an image recognition model.

Abstract: Biomarker of HIV or SIV infected cells and its application are provided. The marker is PLK (polo-like kinase). By inhibiting the activity of a PLK protein or clearing the same, the purpose of releasing viruses without activating a repository is achieved, such that the viruses can be detected in a physiological state and can also be recognized and cleared by an immune system or a drug in vivo. Enhancing the activity of the PLK protein directly inhibits the release of viruses in an HIV and/or SIV-infected cell. The present invention provides a new target for diagnosis and antiviral therapy of HIV and/or SIV infection, provides medication basis and guarantee for the early rapid detection and extremely early treatment of virus infection, and has important clinical value.

Abstract: A medical image processing method is provided to a data processing device. The method includes obtaining a medical image, obtaining a feature map and a medical image content recognition result that correspond to the medical image by using a medical image classification model, or obtaining the feature map and a lesion classification result that correspond to the medical image by using the medical classification model, the feature map including N channels, N being an integer greater than 1, generating a thermodynamic diagram corresponding to the medical image content recognition result or the lesion classification result according to the feature map and a weight parameter set, the weight parameter set including N weight parameters, the weight parameters having a correspondence with the channels, and generating an image recognition result corresponding to the medical image according to the thermodynamic diagram.

Abstract: An aerosol generating apparatus includes: a case comprising a suction nozzle; an atomization component disposed in the case; and a sensing component disposed at the suction nozzle, connected to the atomization component, and configured to generate a sensing signal in response to detecting that a user is in contact with the suction nozzle and transmit the sensing signal to the atomization component.

Abstract: An artificial intelligence-based image processing method includes: obtaining a first sample image of a source domain and a second sample image of a target domain, the first sample image of the source domain carrying a corresponding target processing result; converting the first sample image into a target sample image, the target sample image carrying a corresponding target processing result; training a first image processing model based on the target sample image and the target processing result corresponding to the target sample image, to obtain a second image processing model; and inputting, in response to obtaining a human tissue image of the target domain, the human tissue image into the second image processing model, positioning, by the second image processing model, a target human tissue in the human tissue image, and outputting position information of the target human tissue in the human tissue image.

Abstract: A target positioning method is provided to include: determining, in response to determining that a video frame image including a target lesion is detected from a video stream, location information of the target lesion on the video frame image; tracking the target lesion according to the location information of the target lesion on the video frame image, and determining location information of the target lesion on a video frame image in the video stream.

Abstract: The present disclosure relates to a method and system for three-dimensional reconstruction, and a storage medium. Embodiments regarding three-dimensional reconstruction are described. In an embodiment, the method for three-dimensional reconstruction comprises: obtaining a composite image obtained by photographing a body of a target object and a mirror image of the target object; and performing three-dimensional reconstruction on the target object by using the composite image and relative position and posture information representing a position and posture of a mirror for generating the mirror image relative to a camera for the photography.

Abstract: The present disclosure relates to a three-dimensional reconstruction method and system, and storage medium. Various embodiments regarding three-dimensional reconstruction are described. In an embodiment, a method for training a three-dimensional reconstruction model comprises: generating an initial voxel envelope of a target object based on an image that is obtained by photographing the target object at a plurality of angles of view; performing random sampling on points inside the initial voxel envelope, so as to obtain a set of sampling points; performing global feature extraction on the image, so as to obtain a global feature map; determining, from the global feature map, a global feature corresponding to the sampling point based on a geometric association; performing encoding on geometric information related to the sampling point, so as to generate geometric encoded information; and training the model at least based on the global feature and the geometric encoded information.

Abstract: This application relates to an endoscopic image display method, apparatus, computer device, and storage medium, and relates to the field of machine learning technologies. The method acquiring an endoscopic image; locating a target region image in the endoscopic image, the target region image being a partial image comprising a target region; inputting the target region image into a coding network to obtain a semantic feature of the target region image, the coding network being a part of an image classification network, and the image classification network being a machine learning network obtained through training with first training images; matching the semantic feature of the target region image against semantic features of image samples to obtain a matching result, the matching result indicating a target image sample that matches the target region image; and displaying the endoscopic image and the matching result in an endoscopic image display interface.

Abstract: A medical bed horizontal driving mechanism includes a bracket, a drum, an electric motor, and a position sensor. The bracket is configured to be fixedly connected to a bed frame of a medical bed. The drum is rotatably connected to the bracket. The drum is rotatable to drive a bed board of the medical bed to move relative to the bed frame. The electric motor is arranged in a cavity of the drum. A housing of the electric motor is fixedly connected to the bracket. An output shaft of the electric motor is connected to the drum to drive the drum to rotate relative to the bracket. The position sensor is arranged in the cavity of the drum. The position sensor can detect a rotational position of the drum and generate a position signal, which is used to control the electric motor.

Abstract: A medical image processing method includes: determining a target mask of a target object in a medical image and a reference mask of a reference object in the medical image, the target mask indicating a position and a boundary of the target object, and the reference mask indicating a position and a boundary of the reference object; determining a feature size of the target object based on the target mask; determining a feature size of the reference object based on the reference mask; and determining a target size of the target object based on the feature size of the reference object, a preset mapping relationship between the feature size of the reference object and a reference size, and the feature size of the target object.

Abstract: Biomarker of HIV or SIV infected cells and its application are provided. The marker is PLK (polo-like kinase). By inhibiting the activity of a PLK protein or clearing the same, the purpose of releasing viruses without activating a repository is achieved, such that the viruses can be detected in a physiological state and can also be recognized and cleared by an immune system or a drug in vivo. Enhancing the activity of the PLK protein directly inhibits the release of viruses in an HIV and/or SIV-infected cell. The present invention provides a new target for diagnosis and antiviral therapy of HIV and/or SIV infection, provides medication basis and guarantee for the early rapid detection and extremely early treatment of virus infection, and has important clinical value.

Abstract: Embodiments of this application disclose an image processing method performed by a computer device, and a computer-readable storage medium.

Abstract: A method for training an image recognition model includes: obtaining training image sets; obtaining a first predicted probability, a second predicted probability, a third predicted probability, and a fourth predicted probability based on the training image sets by using an initial image recognition model; determining a target loss function according to the first predicted probability, the second predicted probability, the third predicted probability, and the fourth predicted probability; and training the initial image recognition model based on the target loss function, to obtain an image recognition model.

Abstract: A medical endoscope image recognition method is provided. In the method, endoscope images are received from a medical endoscope. The endoscope images are filtered with a neural network, to obtain target endoscope images. Organ information corresponding to the target endoscope images is recognized via the neural network. An imaging type of the target endoscope images is identified according to the corresponding organ information with a classification network. A lesion region in the target endoscope images is localized according to an organ part indicated by the organ information. A lesion category of the lesion region in an image capture mode of the medical endoscope corresponding to the imaging type is identified.