The present disclosure is directed to a non-intrusive, integrated system comprising an umpire bot for automatically monitoring, umpiring, scoring, analytics, learning and coaching for players while eliminating need for human umpires and scorers. The automated umpire bot with intelligent telescopic function monitors, cognitively recognizes and captures movements from all equipment's, analyses them, moves up and down and even avoid ball collision travelling towards it. The non-intrusive real time system captures all the game moments right from players initiation, toss of coin, commencement of game, monitoring field positions, keeping scores, umpiring decisions, overs, valid/in-valid deliveries, validating balls per over, wickets, catches, boundaries, sixes and displaying scores and statistics all throughout the game.

The present disclosure is directed to a non-intrusive, integrated system comprising an umpire bot for automatically monitoring, umpiring, scoring, analytics, learning and coaching for players while eliminating need for human umpires and scorers. The automated umpire bot with intelligent telescopic function monitors, cognitively recognizes and captures movements from all equipment's, analyses them, moves up and down and even avoid ball collision travelling towards it. The non-intrusive real time system captures all the game moments right from players initiation, toss of coin, commencement of game, monitoring field positions, keeping scores, umpiring decisions, overs, valid/in-valid deliveries, validating balls per over, wickets, catches, boundaries, sixes and displaying scores and statistics all throughout the game.

Diagnosis is assisted by acquiring microscopical observation image data while specifying the position, classifying the image data into histological types with the use of AI, and reconstructing the classification result in a whole lesion. There is provided a pathological diagnosis assisting method that can provide an assistance technology which performs a pathological diagnosis efficiently with satisfactory accuracy by HE staining which is usually used by pathologists. Furthermore, there are provided a pathological diagnosis assisting system, a pathological diagnosis assisting program, and a pre-trained model.

Diagnosis is assisted by acquiring microscopical observation image data while specifying the position, classifying the image data into histological types with the use of AI, and reconstructing the classification result in a whole lesion. There is provided a pathological diagnosis assisting method that can provide an assistance technology which performs a pathological diagnosis efficiently with satisfactory accuracy by HE staining which is usually used by pathologists. Furthermore, there are provided a pathological diagnosis assisting system, a pathological diagnosis assisting program, and a pre-trained model.

An information processing device according to the present invention includes: a memory; and at least one processor coupled to the memory. The processor performs operations. The operations includes: selecting a base image from a base data set that is a set of images including a target region that includes an object that is a target of machine learning and a background region that does not include an object that is a target of the machine learning; generating a processing target image that is a duplicate of the selected base image; selecting the target region included in another image included in the base data set; synthesizing an image of the selected target region with the processing target image; and generating a data set that is a set of the processing target images in which a predetermined number of the target regions are synthesized.

An information processing device according to the present invention includes: a memory; and at least one processor coupled to the memory. The processor performs operations. The operations includes: selecting a base image from a base data set that is a set of images including a target region that includes an object that is a target of machine learning and a background region that does not include an object that is a target of the machine learning; generating a processing target image that is a duplicate of the selected base image; selecting the target region included in another image included in the base data set; synthesizing an image of the selected target region with the processing target image; and generating a data set that is a set of the processing target images in which a predetermined number of the target regions are synthesized.

Deep learning methods and systems for detecting biomarkers within optical coherence tomography volumes using such deep learning methods and systems are provided. Embodiments predict the presence or absence of clinically useful biomarkers in OCT images using deep neural networks. The lack of available training data for canonical deep learning approaches is overcome in embodiments by leveraging a large external dataset consisting of foveal scans using transfer learning. Embodiments represent the three-dimensional OCT volume by “tiling” each slice into a single two dimensional image, and adding an additional component to encourage the network to consider local spatial structure. Methods and systems, according to embodiments are able to identify the presence or absence of AMD-related biomarkers on par with clinicians. Beyond identifying biomarkers, additional models could be trained, according to embodiments, to predict the progression of these biomarkers over time.

Deep learning methods and systems for detecting biomarkers within optical coherence tomography volumes using such deep learning methods and systems are provided. Embodiments predict the presence or absence of clinically useful biomarkers in OCT images using deep neural networks. The lack of available training data for canonical deep learning approaches is overcome in embodiments by leveraging a large external dataset consisting of foveal scans using transfer learning. Embodiments represent the three-dimensional OCT volume by “tiling” each slice into a single two dimensional image, and adding an additional component to encourage the network to consider local spatial structure. Methods and systems, according to embodiments are able to identify the presence or absence of AMD-related biomarkers on par with clinicians. Beyond identifying biomarkers, additional models could be trained, according to embodiments, to predict the progression of these biomarkers over time.

To improve the accuracy of the recognition processing used in an image sensor. A solid-state imaging device includes a pixel array, a converter, an image processing unit, a digital signal processing unit, and a control unit. The pixel array has a plurality of pixels that perform photoelectric conversion. The converter converts an analog pixel signal output from the pixel array into digital image data. The image processing unit performs image processing on the digital image data. The digital signal processing unit performs recognition processing on the digital image data output by the image processing unit. The control unit performs optimization regarding at least one acquisition processing operation among acquisition of the analog pixel signal, acquisition of the digital image data, and acquisition of a result of the recognition processing based on the result of the recognition processing.

To improve the accuracy of the recognition processing used in an image sensor. A solid-state imaging device includes a pixel array, a converter, an image processing unit, a digital signal processing unit, and a control unit. The pixel array has a plurality of pixels that perform photoelectric conversion. The converter converts an analog pixel signal output from the pixel array into digital image data. The image processing unit performs image processing on the digital image data. The digital signal processing unit performs recognition processing on the digital image data output by the image processing unit. The control unit performs optimization regarding at least one acquisition processing operation among acquisition of the analog pixel signal, acquisition of the digital image data, and acquisition of a result of the recognition processing based on the result of the recognition processing.