A method for image colorization includes receiving, from a camera, an input image including a plurality of input image pixels. One or more input interest pixels of the plurality of input image pixels are classified as corresponding to an object of interest. A display image is generated having a plurality of display image pixels each having pixel values based on relative temperature values of objects in a real-world environment, the display image pixels including display interest pixels corresponding to the input interest pixels. The display interest pixels are colorized with a color selected based on a recognized class of the object of interest to give a colorized display image, the selected color being independent of the relative temperature values of the object of interest. The colorized display image is displayed with the display interest pixels colorized with the selected color.

This application provides an automatic machine learning method. The method includes: An AutoML system receives a task target of a user and a first data set, and determines, based on the task target, an initial AI model used to implement the task target for the user; the AutoML system trains the initial AI model based on the received first data set, to obtain a trained AI model, and analyzes the training of the initial AI model based on the first data set, to obtain an analysis result, where the analysis result includes the impact of at least one type of data in the first data set on the training of the initial AI model; and the AutoML system provides an optimization manner of the trained AI model for the user based on the analysis result, where the optimization manner may be: uploading a second data set for optimizing the trained AI model.

A method for displaying cell count information includes steps for: obtaining a cell image; obtaining a first cell count output by applying the cell image to a first counting model; obtaining a second cell count output by applying the cell image to a second counting model; and displaying, on the basis of the first and second cell counts, a screen on which at least either of the first and second cell counts and a correlation between the first and second cell counts are visualized. The first counting model is different from the second counting model.

A false positive removal engine is provided. The false positive removal engine receives detected objects in one or more images. A machine learning classifier computer model, configured with first operational parameters to implement a first operating point, processes the received input to classify each detected object as being a true positive or a false positive to generate a first set of object classifications. If the first set is empty, the false positive removal engine outputs the first set as a filtered list of objects; otherwise the ML classifier computer model is configured with second operational parameters to implement a second operating point, different from the first operating point, which then processes the received input to classify each detected object and generate a second set of objects classified as true positive, which is output by the false positive removal engine as the filtered list of objects.

Systems and methods for image processing are described. Embodiments of the present disclosure receive a training image and a caption for the training image, wherein the caption includes text describing an object in the training image; generate a pseudo mask for the object using a teacher network based on the text describing the object; generate a mask for the object using a student network; and update parameters of the student network based on the mask and the pseudo mask.

A learning image generation device including: a supervised data acquisition unit that acquires supervised data including a learning image and a correct learning image in which a correct region is defined in the learning image as a pair; and a variation learning image generation unit that generates a variation learning image in which a pixel value of a pixel belonging to the correct region is varied within a limitation range of an allowable pixel value of the pixel belonging to the correct region in the learning image

Techniques are described for improving computer-assisted surgical (CAS) systems, particularly, to recognize surgical phases in a video of a surgical procedure. A CAS system includes cameras that provide video stream of a surgical procedure. According to one or more aspects the surgical phases are automatically detected in the video stream using a machine learning model. Particularly, the machine learning model includes a boundary aware cascade stage network to perform surgical phase recognition.

A system and method for training an AI model. A recorded video is divided into video frames that are input and read by a processor that identifies objects in the video frames using the object’s latent characteristics. The processor further classifies an event based on the identified object, the latent characteristics, and surrounding factors at the time the object is identified. Video frames are annotated based on the identified object and classified event. A user’s responses to annotated frames are tracked and the latent characteristics are adjusted based on the user’s responses.

Provided is a method for improving reliability of artificial intelligence-based object recognition, in which: in a server interworking with an artificial intelligence module, one or more object regions included in learning data including an image of a recognition target object to be recognized through the artificial intelligence module are recognized; object region recognition data which sets the recognized object regions is extracted and is provided to user terminals in a designated order; a procedure for receiving, from the user terminals, object region selection data which selects at least one effective object region corresponding to the recognition target object among object regions included in the object region recognition data is performed; and the object region selection data of respective users received from the user terminals is mutually compared and analyzed by the same object region selection data.

Provided are a self-improving object recognition method and system through image capture. The object recognition method includes: collecting a first captured image through a user terminal; predicting image capturing conditions of the first captured image; verifying the first captured image using the predicted image capturing conditions and adding the first captured image to a verified dataset; training an object recognition model using the verified dataset; and acquiring a recognition result of an object indicated by a second captured image using the trained object recognition model.