A medical image processing apparatus according to an embodiment comprises a memory and processing circuitry. The memory is configured to store a plurality of neural networks corresponding to a plurality of imaging target sites, respectively, the neural networks each including an input layer, an output layer, and an intermediate layer between the input layer and the output layer, and each generated through learning processing with multiple data sets acquired for the corresponding imaging target site. The processing circuitry is configured to process first data into second data using, among the neural networks, the neural network corresponding to the imaging target site for the first data, wherein the first data is input to the input layer and the second data is output from the output layer.

A system may determine first groups of image data from multiple groups, obtain a first identification model based on the first groups of image data, and classify the first groups of image data to generate a first classification result based on the first identification model in which the first groups of image data may be classified into a qualified dataset and an unqualified dataset. The system may obtain an initial second identification model with a second accuracy threshold and perform one or more iterations. In each of one or more iterations, the system may classify the unqualified dataset to generate a second classification result, update the qualified dataset and the unqualified dataset, and update, based on the updated qualified dataset, the second identification model. The system may further determine the cleaned dataset based on the updated qualified dataset.

Disclosed are a method and a system for determining a response to a digital task in a computer-implemented crowd-sourced environment. The method comprises determining if a number of the plurality of responses to the digital task received meets a pre-determined minimum answer threshold; in response to the number of the plurality of responses to the digital task meeting the pre-determined minimum answer threshold, executing: for each of the plurality of responses generating, by the server, a confidence parameter representing a probability of an associated one of the plurality of responses being correct; ranking the plurality of responses based on the confidence parameter to determine a top response being associated with a highest confidence parameter; and in response to the highest confidence parameter being above a pre-determined minimum confidence threshold, assigning a value of the top response as a label for the digital task and terminating the digital task execution.

Provided is an information processing apparatus for ordering a plurality of scanned page data with high accuracy. The OCR unit performs optical character recognition for character and layout in a page for each of the plurality of page data. The rule order unit classifies the characters and layouts that are performed optical character recognition by the OCR unit based on the page ordering rules, extracts the page numbers, and calculates the certainty of the page numbers. The ML order unit classifies the page data of pages with low certainty calculated by the rule order unit by machine learning, and it infers the page number.

Provided is an information processing apparatus that easily sets rules for ordering a plurality of scanned page data. The OCR unit performs optical character recognition for character and layout in a page for each of the plurality of page data. The rule order unit classifies the characters and layouts that are performed optical character recognition by the OCR unit based on the page ordering rules, extracts the page numbers, and calculates the certainty of the page numbers. The rule setting unit presents the certainty calculated by the rule order unit to the user and causes the user to set the rule.

Provided is an information processing apparatus that divides a plurality of scanned page data with high accuracy based on the history. The OCR unit performs optical character recognition for character and layout in a page for each of the plurality of page data. The division history unit stores the division history of a plurality of page data on a page-by-page basis based on the classification of characters and layouts that are performed optical character recognition by the OCR unit. The rule order unit classifies each of a plurality of newly scanned page data, and it divides the data in page units by referring to the division history stored in the division history unit.

Provided is an information processing apparatus that divides a plurality of scanned page data with high accuracy. The OCR unit performs optical character recognition for in a page for each of the plurality of page data. The rule order unit classifies each of the plurality of page data based on a page ordering rule according to the characters and the layout recognized by the OCR unit, and it divides the plurality of page data into page units.

In the case where a character on an object is read by using a mobile terminal having a camera function, an image suitable to OCR is acquired in a short time. An information processing apparatus including a camera, which acquires a moving image by capturing a character string on an object by the camera, sets, for each frame making up the acquired moving image, a search area for character recognition for each character making up the character string, detects a candidate character from the search area, determines whether an evaluation value indicating likelihood of the detected candidate character is stable, and outputs a frame of the acquired moving image in response to the evaluation value is determined to be stable.

A medical image processing apparatus according to an embodiment comprises a memory and processing circuitry. The memory is configured to store a plurality of neural networks corresponding to a plurality of imaging target sites, respectively, the neural networks each including an input layer, an output layer, and an intermediate layer between the input layer and the output layer, and each generated through learning processing with multiple data sets acquired for the corresponding imaging target site. The processing circuitry is configured to process first data into second data using, among the neural networks, the neural network corresponding to the imaging target site for the first data, wherein the first data is input to the input layer and the second data is output from the output layer.

A medical image processing apparatus according to an embodiment comprises a memory and processing circuitry. The memory is configured to store a plurality of neural networks corresponding to a plurality of imaging target sites, respectively, the neural networks each including an input layer, an output layer, and an intermediate layer between the input layer and the output layer, and each generated through learning processing with multiple data sets acquired for the corresponding imaging target site. The processing circuitry is configured to process first data into second data using, among the neural networks, the neural network corresponding to the imaging target site for the first data, wherein the first data is input to the input layer and the second data is output from the output layer.