Abstract: An AI-assisted automatic labeling system and a method thereof are disclosed. The method comprises steps: selecting images from microscopic images as candidate images, using a pre-labeling module to automatically label cells in the candidate images, and dividing the labeled images into training data and verification data; using a training module and the training data to train a basic model; using a verification module to verify and modify the basic model, wherein the verification module respectively verifies at least one cell area and at least one background area of the verification data to converge the basic model and form an automatic labeling model; using the automatic labeling model to automatically label cells in redundant images of the microscopic images. The basic model trained by the present invention can use few labeled images to perform regressive training and verification and then automatically labels the redundant images accurately and efficiently.

Abstract: A system for remotely controlling microscopic machinery and a method thereof are provided. The system includes at least one local device and a remote host for sending a control command thereto. A microslide is placed on a microscopic camera device in the local device, and the microscopic camera device captures an image of the microslide according to a capture command in the control command. The local device transmits the image to the remote host in a video format. A motorized stage moves to a next preset position according to a movement command in the control command to capture another image of the microslide. The steps of capturing and transmitting the image and the step of moving are repeated until the microslide is captured completely. The invention can solve the problem of time delay when the image captured by the remote control microscopic camera device is displayed.

Abstract: A system and a method for determining characteristic cells based on image recognition. In the method, a scanning device capturing a full image of the microslide; a host selecting images that comprise stained blocks from the full image; the host sequentially performing image recognition to recognize stained cells of the images, and determining whether the stained cells comprise the characteristic cells with an AI model; and selecting interested images from the images that comprise the characteristic cells, transforming the coordinate system of the interested images into the original coordinate system of the full image, and employing the scanning device to capture the interested images along the Z axis of the original coordinate system of the full image, thereby obtaining and outputting sets of pictures. The present invention can quickly determine whether there are characteristic cells in the tissue under test, so as to provide a diagnostic reference for doctors.

Abstract: A system and a method for cell statistics based on image recognition is disclosed. A microscope captures the full image of a microslide, selects an interest region from the full image, and divides the region into sub-regions. A camera scans and transits the sub-regions to a host. The host performs image recognition on the scanned images to recognize various cells, and accumulates the number of the various cells in each of the scanned images to obtain a cell ratio of each type of the cells. When the number is accumulated and a difference among the cell ratios of the N consecutive scanned images is less than a preset value, the host stops scanning the remains of the sub-regions and outputs the cell ratio of each type of the presently-accumulated cells. The method can obtain various cell ratios of tissue samples for diagnosis.

Abstract: A high-speed automatic scanning system for interpreting images with AI assistance and method using the same are provided. The system includes a control computer and an image capture platform. Scanning parameters of the image capture platform, including a helically-clockwise or helically-counterclockwise scanning path, is set by the control computer. After the scanning path is selected, the image capture platform aligns a camera to focus on a central block and respectively captures images of the central block's sub-blocks. Until all the blocks have been scanned, the image capture platform repeats the following procedure: moving the focusing position to a next neighboring block according to the scanning path, focusing on the next neighboring block, and capturing images of the sub-blocks of the next neighboring block. The present invention can fast perform scanning, exempted from performing focusing for every image, reducing the cycles and time of focusing.

Abstract: An AI-assisted automatic labeling system and a method thereof are disclosed. The method comprises steps: selecting images from microscopic images as candidate images, using a pre-labeling module to automatically label cells in the candidate images, and dividing the labeled images into training data and verification data; using a training module and the training data to train a basic model; using a verification module to verify and modify the basic model, wherein the verification module respectively verifies at least one cell area and at least one background area of the verification data to converge the basic model and form an automatic labeling model; using the automatic labeling model to automatically label cells in redundant images of the microscopic images. The basic model trained by the present invention can use few labeled images to perform regressive training and verification and then automatically labels the redundant images accurately and efficiently.