A method and electronic device are provided for processing data. Analog text included in a document is detected. A first area of the analog text that converts to digital text and a second area of the analog text that does not convert to the digital text are determined. The digital text is displayed in the first area that converts to the digital text, and an image of at least a portion of the analog text is displayed in the second area that does not convert to digital text.

A method is disclosed. The method includes providing an imaging apparatus, recording image data of an imaging location using the imaging apparatus, displaying the image data to a user via a user device, selecting an image object from the image data based on a selection criteria, and determining whether or not a selection criteria error of the image object is to be checked. The method also includes displaying a bounding shape, which bounds the image object, to the user via the user device when the selection criteria error is to be checked, prompting the user to enter user input indicating whether or not the selection criteria error is present, and storing data of the image object in a cache when the user input indicates that the selection criteria error is present.

This application discloses a method and an apparatus for selecting a face image, a device, and a storage medium and relates to the field of artificial intelligence technologies. The method includes detecting, after a frame of face image is obtained, whether the face image meets a preliminary quality screening condition; determining, in response to a first face image meeting the preliminary quality screening condition, an overall quality score of the first face image, the overall quality score representing overall quality of the face image; and transmitting the first face image to a face recognition process in response to the overall quality score of the first face image being greater than a level-one threshold.

Provided are an information processing device, a display method, and a program which enable easy recognition of the discrepancy between the detection result by detection processing for an image captured by an endoscope and the detection result by a user.

An information processing device (1) includes: a first acquisition unit (2) configured to acquire a capturing time of a lesion image instructed to be saved by a user, from a series of images captured by an endoscope during examination with the endoscope; a second acquisition unit (3) configured to acquire a capturing time of a lesion image detected by detection processing for the series of images captured by the endoscope during the examination; and a display control unit (4) configured to cause a display device to display a first capturing time and a second capturing time which are plotted on a time axis, the first capturing time being the capturing time acquired by the first acquisition unit (2), the second capturing time being the capturing time acquired by the second acquisition unit (3).

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.

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.

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.

A method of wood shingle classification and sawing using machine vision comprises the steps of taking an image of a wood slab in a wood block and identifying a defect in that slab; comparing an image of this defect to images of confirmed defects in a database of confirmed defects to find a match of the defect in these images. If a match is not found, sawing a shingle from the slab and classifying the shingle while making abstraction of the defect. In a second aspect, when images of two consecutive shingles are identical, a third and subsequent shingles can be sawn from a block without taking images thereof. In another aspect, the comparing of images is done by an artificial intelligence system that is trained on a database of images that are associable to the subjectivity of experienced shingle sawyers.

An apparatus for managing data used for object recognition includes an image capturing unit configured to capture an image of an object, a storage unit storing image data of products registered for sale, an operation panel configured to receive a user selection, and a processor configured to determine the products registered for sale that are similar to the object, based on the captured image and the image data, display the similar products on the operation panel as user selectable items, determine one or more of the similar products designated by the user selection, and invalidate the designated similar products so that efficiency of object recognition by the apparatus can be improved.