Systems and methods are provided for medical image classification of images from varying sources. A set of microscopic medical images are acquired, and a first neural network module configured to reduce each of the set of microscopic medical images to a feature representation is generated. The first neural network module, a second neural network module, and a third neural network module are trained on at least a subset of the set of microscopic medical images. The second neural network module is trained to receive feature representation associated with an image of the microscopic images and classify the image into one of a first plurality of output classes. The third neural network module is trained to receive the feature representation, classify the image into one of a second plurality of output classes based on the feature representation, and provide feedback to the first neural network module.

Systems and methods are provided for medical image classification of images from varying sources. A set of microscopic medical images are acquired, and a first neural network module configured to reduce each of the set of microscopic medical images to a feature representation is generated. The first neural network module, a second neural network module, and a third neural network module are trained on at least a subset of the set of microscopic medical images. The second neural network module is trained to receive feature representation associated with an image of the microscopic images and classify the image into one of a first plurality of output classes. The third neural network module is trained to receive the feature representation, classify the image into one of a second plurality of output classes based on the feature representation, and provide feedback to the first neural network module.

According to the present disclosure, a handwritten signature to be authenticated is received, a plurality of pieces of signature behavioral characteristic information are extracted, all of the plurality of the pieces of the extracted signature behavioral characteristic information are applied to each of first and second signature authentication algorithms using different techniques to analyze a degree of matching between the received handwritten signature and a registered handwritten signature, results of analysis performed by the first and second signature authentication algorithms are combined to adjust a false rejection rate and a false acceptance rate, and whether handwritten signature authentication succeeds is finally determined.

According to the present disclosure, a handwritten signature to be authenticated is received, a plurality of pieces of signature behavioral characteristic information are extracted, all of the plurality of the pieces of the extracted signature behavioral characteristic information are applied to each of first and second signature authentication algorithms using different techniques to analyze a degree of matching between the received handwritten signature and a registered handwritten signature, results of analysis performed by the first and second signature authentication algorithms are combined to adjust a false rejection rate and a false acceptance rate, and whether handwritten signature authentication succeeds is finally determined.

An image retrieval method, an image retrieval device, and an image retrieval system, which are used for image retrieval, are provided. The image retrieval method comprises: receiving a first original image (S100); extracting an image feature of the first original image to obtain a first feature code (S200); obtaining first target information according to the first feature code (S300); according to the first target information, searching for a first target painting set corresponding to the first target information in at least one of a painting library and a knowledge graph library, so as to obtain the first target painting set (S400); and outputting the first target painting set (S500).

An image retrieval method, an image retrieval device, and an image retrieval system, which are used for image retrieval, are provided. The image retrieval method comprises: receiving a first original image (S100); extracting an image feature of the first original image to obtain a first feature code (S200); obtaining first target information according to the first feature code (S300); according to the first target information, searching for a first target painting set corresponding to the first target information in at least one of a painting library and a knowledge graph library, so as to obtain the first target painting set (S400); and outputting the first target painting set (S500).

A data collection system according to the present disclosure includes: a sensor device that collects data; and a server device including a learning model that performs output according to a learning result, corresponding to input, and a data analysis unit that specifies data that is beneficial for or lacking in training of the learning model. The server device transmits, to the sensor device, a request signal for collecting data that is beneficial for or lacking in the training specified by the data analysis unit, or data similar to the data, the sensor device collects data that is beneficial for or lacking in the training, or similar data based on the received request signal, and transmits the collected data to the server device, and the server device retrains the learning model based on the data transmitted from the sensor device.

A data collection system according to the present disclosure includes: a sensor device that collects data; and a server device including a learning model that performs output according to a learning result, corresponding to input, and a data analysis unit that specifies data that is beneficial for or lacking in training of the learning model. The server device transmits, to the sensor device, a request signal for collecting data that is beneficial for or lacking in the training specified by the data analysis unit, or data similar to the data, the sensor device collects data that is beneficial for or lacking in the training, or similar data based on the received request signal, and transmits the collected data to the server device, and the server device retrains the learning model based on the data transmitted from the sensor device.

A system and method for training a text report identification machine learning model and an image identification machine learning model, including: initially training a text report machine learning model, using a labeled set of text reports including text pre-processing the text report and extracting features from the pre-processed text report, wherein the extracted features are input into the text report machine learning model; initially training an image machine learning model, using a labeled set of images; applying the initially trained text report machine learning model to a first set of unlabeled text reports with associated images to label the associated images; selecting a first portion of labeled associated images; re-training the image machine learning model using the selected first portion of labeled associated images; applying the initially trained image machine learning model to a first set of unlabeled images with associated text reports to label the associated text reports; selecting a first portion of labeled associated text reports; and re-training the text report machine learning model using the selected first portion of labeled associated text reports.

Methods and systems disclosed herein relate generally to processing images to estimate whether at least part of a tumor is represented in the images. A computer-implemented method includes accessing an image of at least part of a biological structure of a particular subject, processing the image using a segmentation algorithm to extract a plurality of image objects depicted in the image, determining one or more structural characteristics associated with an image object of the plurality of image objects, processing the one or more structural characteristics using a trained machine-learning model to generate estimation data corresponding to an estimation of whether the image object corresponds to a lesion or tumor associated with the biological structure, and outputting the estimation data for the particular subject.