Methods and systems are described for generating dynamic conversational responses using two-tier machine learning models. The dynamic conversational responses may be generated in real time and reflect the likely goals and/or intents of a user. The two-tier machine learning model may include a first tier that determines an intent cluster based on a feature input, and a second tier that determines a specific intent from the cluster.

Provided is an electronic device including: a storage unit for storing a plurality of image recognition models each of which is defined by an item of learning content unique thereto, with each of which at least one item of identifier information can be inferred by using the item of learning content, and each of which is linked with an item of attribute information unique thereto; a destination-attribute-information identifying unit that identifies at least one item of attribute information as a destination of image information from among items of attribute information stored in the storage unit, on the basis of an operation; an image-recognition-model selecting unit that selects the image recognition model linked with the identified item of attribute information; and an identifier-information inferring unit that inputs the image information to the image recognition model selected by the image-recognition-model selecting unit and that infers an item of identifier information.

Provided is a method for performing a prediction work on a target image, including dividing the target image into a plurality of sub-images, generating prediction results for a plurality of pixels included in each of the plurality of divided sub-images, applying weights to the prediction results for the plurality of pixels, and merging the prediction results for the plurality of pixels applied with the weights.

A system, method, and computer-accessible medium for using medical imaging data to screen for a cystic lesion(s) can include, for example, receiving first imaging information for an organ(s) of a one patient(s), generating second imaging information by performing a segmentation operation on the first imaging information to identify a plurality of tissue types, including a tissue type(s) indicative of the cystic lesion(s), identifying the cystic lesion(s) in the second imaging information, and applying a first classifier and a second classifier to the cystic lesion(s) to classify the cystic lesion(s) into one or more of a plurality of cystic lesion types. The first classifier can be a Random Forest classifier and the second classifier can be a convolutional neural network classifier. The convolutional neural network can include at least 6 convolutional layers, where the at least 6 convolutional layers can include a max-pooling layer(s), a dropout layer(s), and fully-connected layer(s).

The purpose of the present invention is to detect an object in images accurately by means of image recognition without using a special device for removing the influence of the parallax between a plurality of images. An image transformation unit (401) transforms a plurality of images acquired by an image acquisition unit (407). A reliability level calculation unit (402) calculates a level of reliability representing how small the misalignment between images is. A score calculation unit (405) calculates a total score taking into account both an object detection score based on a feature quantity calculated by a feature extraction unit (404), and the level of reliability calculated by the reliability level calculation unit (402). An object detection unit (406) detects an object in the images on the basis of the total score.

In accordance with some embodiments, systems, methods, and media for automatically segmenting and diagnosing prostate lesions using multi-parametric magnetic resonance imaging (mp-MRI) data are provided. In some embodiments, the system comprises is programmed to: receive mp-MRI data depicting a prostate, including T2w data and ADC data; provide the T2w data and ADC data as input to first and second input channels of a trained convolutional neural network (CNN); receive, from the trained CNN, output values from output channels indicating which pixels are likely to correspond to a particular class of prostate lesion, the channels corresponding to predicted aggressiveness in order of increasing aggressiveness, identify a prostate lesion in the data based on output values greater than a threshold; predict an aggressiveness based on which channel had values over the threshold; and present an indication that a prostate lesion of the predicted aggressiveness is likely present in the prostate.

Discussed generally are techniques for managing operation of programs in a sequential order. A method can include receiving a query for an image, the query indicating characteristics of the image, selecting a chain of algorithms configured to identify the image based on the characteristics, operating an algorithm of the selected chain of algorithms that operate in increased fidelity order on an input to produce a first result, operating a ground truth algorithm on the input to generate a second result, comparing the first and second results to determine a probability of correctness (Pc) and confidence interval (CI) for the algorithm, and altering the chain of algorithms based on the determined Pc and CI.

This application relates to the artificial intelligence field, and provides an image processing method, an apparatus, and a system. The image processing method includes: obtaining a plurality of image blocks by segmenting a to-be-analyzed pathological image; inputting the plurality of image blocks to a first analysis model to obtain a first analysis result, where the first analysis model classifies each of the plurality of image blocks based on a quantity or an area of suspicious lesion components; inputting at least one second-type image block in the first analysis result to a second analysis model to obtain a second analysis result, where the second analysis model analyzes a location of a suspicious lesion component of each input second-type image block; and obtaining a final analysis result of the pathological image based on the first analysis result and the second analysis result.

A system includes first and second sensors and a controller. The first sensor is of a first type and is configured to sense objects around a vehicle and to capture first data about the objects in a frame. The second sensor is of a second type and is configured to sense the objects around the vehicle and to capture second data about the objects in the frame. The controller is configured to down-sample the first and second data to generate down-sampled first and second data having a lower resolution than the first and second data. The controller is configured to identify a first set of the objects by processing the down-sampled first and second data having the lower resolution. The controller is configured to identify a second set of the objects by selectively processing the first and second data from the frame.

Certain aspects of the present disclosure provide techniques for processing with an auto exiting machine learning model architecture, including processing input data in a first portion of a classification model to generate first intermediate activation data; providing the first intermediate activation data to a first gate; making a determination by the first gate whether or not to exit processing by the classification model; and generating a classification result from one of a plurality of classifiers of the classification model.