Provided is an object detection device including: a detection unit configured to detect objects for every detection period to output detection information containing a reliability for each of the detected objects; a determination unit configured to: increment a detection count for each of the objects; calculate, for each of the objects, a sum of latest N reliabilities in the detection period; and determine, as a normally recognized object, an object for which the sum is equal to or larger than a first threshold value, which is set in advance depending on the detection count; and a control unit configured to output, as normally detected object information, detection information on the normally recognized object.

The invention relates to inferring the state of a system of interest having a plurality of indicator values and possibly being heterogeneous in nature. A number of indicator values from a control state and from a comparison state are gathered. From these indicator values, classification power between the control and comparison states (measure of goodness) is computed. Difference values are computed for the indicator values from the system of interest based on the difference to the indicator values from control and comparison states. From a number of these indicators, composite indicators are formed, and composite measures of goodness and composite difference values are computed. A plurality of composite indicators may be formed at different levels. These indicators may be represented as a tree and grouped according to content, and at the same time they may be arranged according to the measure of goodness or some other value. The indicators, measures of goodness, and difference values may be visualized and shown to a user, who may use such a representation for inferring the state of the system.

Systems and methods for improvements in AI model learning and updating are provided. The model updating may reuse existing business conversations as the training data set. Features within the dataset may be defined and extracted. Models may be selected and parameters for the models defined. Within a distributed computing setting the parameters may be optimized, and the models deployed. The training data may be augmented over time to improve the models. Deep learning models may be employed to improve system accuracy, as can active learning techniques. The models developed and updated may be employed by a response system generally, or may function to enable specific types of AI systems. One such a system may be an AI assistant that is designed to take use cases and objectives, and execute tasks until the objectives are met. Another system capable of leveraging the models includes an automated question answering system utilizing approved answers. Yet another system for utilizing these various classification models is an intent based classification system for action determination. Lastly, it should be noted that any of the above systems may be further enhanced by enabling multiple language analysis.

Embodiments discussed herein facilitate determination of one of a probability of prostate cancer recurrence-free survival or a risk factor associated with prostate cancer based on intra-tumor stromal morphology. Example embodiments can perform operations comprising: accessing a digitized histological image of a prostate of a patient, wherein the histological image comprises a region of interest associated with prostate cancer; identifying nuclei of intra-tumoral stromal cells within the region of interest; extracting, for the region of interest of the digitized histological image, one or more features describing the structure of the intra-tumoral stromal cells; and generating, via a model based at least on the one or more features, one of a probability of prostate cancer recurrence-free survival or a risk score associated with prostate cancer for the patient based at least on the extracted one or more features.

Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for acquiring an image. The method includes distilling an original image set through a capsule neural network model to generate a distilled image set, wherein the distilled image set includes a plurality of distilled images. The method further includes acquiring a first feature of a first image through the capsule neural network model. The method further includes acquiring a plurality of distilling features of the plurality of distilled images respectively through the capsule neural network model. The method further includes determining a plurality of similarities between the first feature and the plurality of distilling features respectively. The method further includes acquiring at least one original image matching the first image based on the plurality of similarities.

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.

A computer-implemented method for quadratic classification involves generating a data-driven likelihood ratio test based on a dual locus of likelihoods and principal eigenaxis components that contains Bayes' likelihood ratio and automatically generates the best quadratic decision boundary. A dual locus of likelihoods and principal eigenaxis components, formed by a locus of weighted reproducing kernels of extreme points, satisfies fundamental statistical laws for a quadratic classification system in statistical equilibrium and is the basis of an optimal quadratic system for which the eigenenergy and the Bayes' risk are minimized, so that the classification system achieves Bayes' error rate and exhibits optimal generalization performance. Quadratic classification systems can be linked with other such systems to perform multiclass quadratic classification and to fuse feature vectors from different data sources. Quadratic classification systems also provide a practical statistical gauge that measures data distribution overlap and Bayes' error rate.

Example methods and systems for radiotherapy treatment planning are provided. One example method may comprise obtaining image data associated with a patient; and processing the image data to generate a treatment plan for the patient using an inferential chain that includes multiple AI engines that are trained separately to perform respective multiple treatment planning steps. A first treatment planning step may be performed using a first AI engine to generate first output data based on at least one of: (i) the image data, and (ii) first input data generated based on the image data. A second treatment planning step may be performed using a second AI engine to generate the treatment plan based on at least one of: (i) the first output data, and (ii) second input data generated based on the first output data.

Example methods and systems for generating an aggregated artificial intelligence (AI) engine for radiotherapy treatment planning are provided. One example method may include obtaining multiple AI engines associated with respective multiple treatment planners; generating multiple sets of output data using the multiple AI engines associated with the respective multiple treatment planners: comparing the multiple AI engines associated with the respective multiple treatment planners based on the multiple sets of output data; and based on the comparison, aggregating at least some of the multiple AI engines to generate the aggregated AI engine for performing the particular treatment planning step. The multiple AI engines may be trained to perform a particular treatment planning step, and each of the multiple AI engines is trained to emulate one of the multiple treatment planners performing the particular treatment planning step.

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.