A computer-implemented method includes preprocessing a variable dimension medical image, identifying one or more areas of interest in the medical image; and analyzing the one or more areas of interest using a deep learning model. A computing system includes one or more processors; and one or more memories storing instructions that, when executed by the one or more processors, cause the computing system to preprocess a variable dimension medical image, identify one or more areas of interest in the medical image; and analyze the one or more areas of interest using a deep learning model. A non-transitory computer readable medium contains program instructions that when executed, cause a computer to preprocess a variable dimension medical image, identify one or more areas of interest in the medical image, and analyze the one or more areas of interest using a deep learning model.

Certain aspects of the present disclosure provide techniques for automatically detecting and classifying tumor regions in a tissue slide. The method generally includes obtaining a digitized tissue slide from a tissue slide database and determining, based on output from a tissue classification module, a type of tissue of shown in the digitized tissue slide. The method further includes determining, based on output from a tumor classification model for the type of tissue, a region of interest (ROI) of the digitized tissue slide and generating a classified slide showing the ROI of the digitized tissue slide and an estimated diameter of the ROI. The method further includes displaying on an image display unit, the classified slide and user interface (UI) elements enabling a pathologist to enter input related to the classified slide.

An object-extraction method includes generating multiple partition objects based on an electronic document, and receiving a first user selection of a data element via a user interface of a compute device. In response to the first user selection, and using a machine learning model, a first subset of partition objects from the multiple partition objects is detected and displayed via the user interface. A user interaction, via the user interface, with one of the partition objects is detected, and in response, a weight of the machine learning model is modified, to produce a modified machine learning model. A second user selection of the data element is received via the user interface, and in response and using the modified machine learning model, a second subset of partition objects from the multiple partition objects is detected and displayed via the user interface, the second subset different from the first subset.

Systems for controlling scanning devices and capturing image data are provided. In some examples, image data may be received by a computing platform. The image data may be received via a first network and from another computing platform in wired communication with a scanning device. In some arrangements, image quality analysis may be performed and data may be extracted from the image data. The results of the data analysis, as well as the image data and extracted data may be transmitted, via a second network different from the first network, to an associate computing device. In some examples, input received by the associate computing device may be transmitted to the computing platform indicating whether the image is approved or rejected for further processing. If the image is approved, the image data and extracted data may be transmitted, via the second network, to another device for matching and further processing functions.

Provided is a ledger recognition system which can enhance recognition accuracy of a handwritten character filled out by a user thus capable of reducing a manual work in a correction operation. A ledger recognition system includes: a headquarter server configured to recognize handwritten characters described in a ledger by a user; a system terminal including an image scanner for reading the handwritten characters filled out in the ledger by the user; and a public telecommunication network which allows the headquarter server and the system terminal to be communicably connected with each other. The headquarter server includes a handwritten character recognition unit where the handwritten character recognition unit receives the image data of the ledger read by the image scanner from the system terminal, recognizes the handwritten characters written by the user in the image data of the received ledger in accordance with at least two types of OCR recognition programs having different algorithms, determines the handwritten characters described in the ledger with respect to a part of the handwritten characters where recognition results in accordance with the OCR recognition programs agree with each other, and sets a part of the handwritten characters where the recognition results by the OCR recognition programs do not agree with each other as an object of correction processing.

An artificial intelligence apparatus for recognizing an object included in image data can include a camera, a communication modem, a memory configured to store an image recognition model, a natural language processing (NLP) model, and an NLP model-based image recognition model learned based on the NLP model, and a processor is configured to receive image data from the camera or the communication modem, in response to recognizing an object included in the image data using the image recognition model, generate first recognition information on the object included in the image data, and in response to the recognizing the object included in the image data using the image recognition model being unsuccessful, generate second recognition information on the object included in the image data based on recognizing the object using the NLP model-based image recognition model.

A method for lung nodule evaluation is provided. The method may include obtaining a target image including at least a portion of a lung of a subject. The method may also include segmenting, from the target image, at least one target region each of which corresponds to a lung nodule of the subject. The method may further include generating an evaluation result with respect to the at least one lung nodule based on the at least one target region.

An information-processing device, when image recognition performed by an object recognition function and a first category recognition function on a captured image acquired from an image capture display device fails, and image recognition performed by a second category recognition function succeeds, informs a user of a method for capturing an image that enables object recognition, and causes the object recognition function to perform image recognition on another captured image that is captured in accordance with the method. If the image recognition performed by the object recognition unit on the other captured image succeeds, information-processing device instructs image capture display device to display a composite image determined according to a result of the image recognition at a position determined according to the result of the image recognition.

In a rectangular region detection mechanism, a to-be-processed image information is received, where the to-be-processed image information comprises at least two images, and where the at least two images comprise a same plurality of first feature points. A plurality of first edge line segments in one of the at least two images are detected. Four first edge line segments from the plurality of first edge line segments are determined. Locations of photographed points corresponding to the plurality of first feature points in a region formed by the four first edge line segments are determined based on location information of the plurality of first feature points. The region is determined as a rectangular region when the photographed points corresponding to the plurality of first feature points in the region are coplanar.

An object-extraction method includes generating multiple partition objects based on an electronic document, and receiving a first user selection of a data element via a user interface of a compute device. In response to the first user selection, and using a machine learning model, a first subset of partition objects from the multiple partition objects is detected and displayed via the user interface. A user interaction, via the user interface, with one of the partition objects is detected, and in response, a weight of the machine learning model is modified, to produce a modified machine learning model. A second user selection of the data element is received via the user interface, and in response and using the modified machine learning model, a second subset of partition objects from the multiple partition objects is detected and displayed via the user interface, the second subset different from the first subset.