A medical image processing device including a processor configured to extract a feature value from a medical image; perform recognition processing of the medical image based on the feature value; and provide the feature value and a result of the recognition to a machine learning device that performs learning using the feature value and the result of the recognition as the learning data.

In some aspects, systems and methods for rapidly building, managing, and sharing machine learning models are provided. Managing the lifecycle of machine learning models can include: receiving a set of unannotated data; requesting annotations of samples of the unannotated data to produce an annotated set of data; building a machine learning model based on the annotated set of data; deploying the machine learning model to a client system, wherein production annotations are generated; collecting the generated production annotations and generating a new machine learning model incorporating the production annotations; and selecting one of the machine learning model built based on the annotated set of data or the new machine learning model.

A discrimination device includes a sub-data set extraction unit for extracting from a plurality of labeled learning data a sub-learning data set to be used for learning and a sub-verification data set to be used for verification, a learning unit for performing supervised learning on the basis of the sub-learning data set to generate a pre-trained model for discriminating a label from data related to an object, a discrimination unit for conducting a discrimination processing using the pre-trained model on each piece of learning data contained in the sub-verification data set, a verification result recording unit for recording a result of the discrimination processing in association with the learning data, and a correctness detection unit for detecting learning data attached with a label that may be incorrect based on the discrimination processing results recorded in association with respective learning data.

A method for user authentication based on feature information includes: judging whether a user to be authenticated belongs to a similar user group, wherein the similar user group comprises at least two similar users, and the similar users are users whose reference feature information meets a preset similarity condition and a preset distinguishability condition; and authenticating the user to be authenticated according to reference feature information in the similar user group if the user to be authenticated belongs to the similar user group.

An image processing device according to one of the exemplary aspects of the present invention includes: a scale space generation means for generating the scaled samples from a given input region of interest; feature extraction means for extracting features from the scale samples; a likelihood estimation means for deriving an estimated probability distribution of the scaled samples by maximizing the likelihood of a given scaled sample and the parameters of the distribution; a probability distribution learning means for updating the model parameters given the correct distribution of the scaled samples; a template generation means to combine the previous estimates of the object features into a single template which represents the object appearance; an outlier rejection means to remove samples which have a probability below the threshold; and a feature matching means for obtaining the similarity between a given template and a scaled sample and selecting the sample with the maximum similarity as the final output.

A computer implemented method of generating at least one shape of a region of interest in a digital image is provided. The method includes obtaining, by an image processing engine, access to a digital tissue image of a biological sample; tiling, by the image processing engine, the digital tissue image into a collection of image patches; identifying, by the image processing engine, a set of target tissue patches from the collection of image patches as a function of pixel content within the collection of image patches; assigning, by the image processing engine, each target tissue patch of the set of target tissue patches an initial class probability score indicating a probability that the target tissue patch falls within a class of interest, the initial class probability score generated by a trained classifier executed on each target tissue patch; generating, by the image processing engine, a first set of tissue region seed patches by identifying target tissue patches having initial class probability scores that satisfy a first seed region criteria, the first set of tissue region seed patches comprising a subset of the set of target tissue patches; generating, by the image processing engine, a second set of tissue region seed patches by identifying target tissue patches having initial class probability scores that satisfy a second seed region criteria, the second set of tissue region seed patches comprising a subset of the set of target tissue patches; calculating, by the image processing engine, a region of interest score for each patch in the second set of tissue region seed patches as a function of initial class probability scores of neighboring patches of the second set of tissue region seed patches and a distance to patches within the first set of issue region seed patches; and generating, by the image processing engine, one or more region of interest shapes by grouping neighboring patches based on their region of interest scores.

Techniques are provided for increasing the accuracy of automated classifications produced by a machine learning engine. Specifically, the classification produced by a machine learning engine for one photo-realistic image is adjusted based on the classifications produced by the machine learning engine for other photo-realistic images that correspond to the same portion of a 3D model that has been generated based on the photo-realistic images. Techniques are also provided for using the classifications of the photo-realistic images that were used to create a 3D model to automatically classify portions of the 3D model. The classifications assigned to the various portions of the 3D model in this manner may also be used as a factor for automatically segmenting the 3D model.

The present disclosure discloses a sample acquisition method, a target detection model generation method, a target detection method, a computing device, and a computer readable medium. The sample acquisition method includes: adding a perturbation to a pre-marked sample original box in an original image to obtain a sample selection box, wherein an image framed by the sample original box contains a target; and extracting an image framed by the sample selection box as a sample. The technical solutions of the present disclosure can effectively increase the number of the samples that can be acquired in the original image, and adding a background to the samples can effectively improve the recognition accuracy of the trained target detection model.

A mechanism is provided in a data processing system comprising a processor and a memory, the memory comprising instructions executed by the processor to specifically configure the processor to implement a statistical model tool for providing insight into decision making. The statistical model tool applies the statistical model to an input image to generate an original classification probability. An image modification component executing within the statistical model tool iterative modifies each portion of the input image to generate a modified image. The statistical model tool applies the statistical model to the modified image to generate a new classification probability for each portion of the input image. A compare component executing in the statistical model tool compares each new classification probability to the original classification probability to generate a respective probability distance. A distance map generator executing within the statistical model tool generates a distance map data structure based on the probability distances. The distance map data structure represents an impact each portion of the input image has on determining classification probability by the statistical model.

A method for managing a smart database which stores facial images for face recognition is provided. The method includes steps of: a managing device (a) counting specific facial images corresponding to a specific person in the smart database where new facial images are continuously stored, and determining whether a first counted value, representing a count of the specific facial images, satisfies a first set value; and (b) if the first counted value satisfies the first set value, inputting the specific facial images into a neural aggregation network, to generate quality scores of the specific facial images by aggregation of the specific facial images, and, if a second counted value, representing a count of specific quality scores among the quality scores from a highest during counting thereof, satisfies a second set value, deleting part of the specific facial images, corresponding to the uncounted quality scores, from the smart database.