Abstract: A method for training an artificial neural network for detecting a prostate cancer from a TURP pathological image, includes: acquires a plurality of acquiring pathological images for primary training, each being a prostate needle biopsy pathological image or a radical prostatectomy pathological image; using the pathological images to primarily train an artificial neural network for determining prostate cancer; acquiring TURP pathological images; and using the TURP pathological images to secondarily train the primarily trained artificial neural network, wherein each TURP pathological image includes a non-prostate tissue region and/or a cauterized prostate tissue region, and does not include any prostate cancer lesion region.

Abstract: A bladder lesion diagnosis method using a learned neural network, and a system thereof. The bladder lesion diagnosis method using a neural network includes the steps of: receiving a unit pathological image by a bladder lesion diagnosis system; inputting, by the bladder lesion diagnosis system, the unit pathological image into a first neural network to obtain the diagnosis result of a first bladder lesion among a plurality of bladder lesions in the unit pathological image; and inputting, by the bladder lesion diagnosis system, the unit pathological image into a second neural network to obtain the diagnosis result of a second bladder lesion, other than the first bladder lesion, among the plurality of bladder lesions in the unit pathological image.

Abstract: Disclosed are a method for refining a tissue image by removing, from a slide image of a tissue specimen, a tissue specimen region determined to be another tissue specimen, and a computer system performing same. According to one aspect of the present invention, provided is a method for refining a tissue specimen image, comprising the steps of: extracting a plurality of contours corresponding to a plurality of tissue regions included in a tissue specimen image; calculating the center point coordinates of each of the extracted plurality of contours; determining a main tissue contour from among the plurality of contours, on the basis of the center point coordinates of the tissue specimen image and the center point coordinates of each of the plurality of contours; and removing a region corresponding to at least a part of the contours other than the main tissue contour among the plurality of contours.

Abstract: An allocation search device includes: a first sample output unit that outputs, on the basis of past event occurrence data, a first main sample that may occur under a predetermined condition and a first auxiliary sample that may occur under a similar condition; an allocation plan creation unit that creates a plurality of allocation plans of resources for an occurring event; a first allocation evaluation unit that evaluates whether or not each of the plurality of allocation plans satisfies a predetermined evaluation criterion in a case where the first main sample and the first auxiliary sample are applied to each of the plurality of allocation plans; a second sample output unit that outputs, on the basis of latest event occurrence data, a future second main sample that may occur under the predetermined condition and a future second auxiliary sample that may occur under the similar condition; and a second allocation evaluation unit that, in a case where the second main sample and the second auxiliary sample are

Abstract: A display control device controls a display unit to display position information of an emergency vehicle, a predictive distribution of an occurrence point, and a risk level, the occurrence point representing a point at which a call for the emergency vehicle occurs, the risk level being determined depending on a time required for the emergency vehicle to arrive at the occurrence point after the call for the emergency vehicle occurs or a distance between the emergency vehicle and the occurrence point.

Abstract: A comparison unit 68 compares target data to be inferred with a learning data group that is data used for learning of the inference model, and determines that an inference result is uncertain when a comparison result does not satisfy a fixed criterion. A notification unit 70 notifies a user that the inference result is uncertain in addition to the inference result when the inference result is determined to be uncertain.

Abstract: A business support device acquires at least business information including order reception/loss information relating to each commercial material for a company that is a business target of the commercial material, area information of a company, a business type of the company, and a company scale of the company, generates data used for prediction of an order reception score for each company and for each commercial material, predicts the order reception score for each commercial material and for each company using the generated data, and displays the predicted order reception score.

Abstract: A waypoint setting device sets a waypoint on the basis of a predictive distribution representing a demand prediction of occurrence points indicating positions from which a mobile object is called, position information indicating a position of a target mobile object that is a mobile object for which the waypoint is to be set, and destination information indicating information regarding a destination to which the target mobile object is to be moved. Then, the waypoint setting device outputs the set waypoint.

Abstract: A hospital that has a high possibility of accepting a user can be predicted with good precision. An estimating method is an estimating method of estimating a prospect of a hospital accepting a user at a first time. In the estimating method, a prospect of accepting the user at the first time is estimated by correlating a reason of the hospital rejecting acceptance of a user at a second time, and an amount of time from the second time to the first time.

Abstract: Features serving as a basis for prediction can be explained. In an explanation creating method, with regard to a prospect of each of hospitals accepting a user, prediction results are found in advance using each of features including information relating to a reason of each of the hospitals rejecting acceptance of the user. Processing prescribed in advance is performed regarding each of features to be taken as an object, out of each of the features, and finds prediction results following modification. On the basis of a difference between prediction results using each of the features and prediction results following modification found regarding the features to be taken as an object, in a case in which there is a difference that satisfies a standard set in advance regarding each of the features to be taken as an object, creating an explanation regarding prediction results relating to the reason of rejection regarding this feature.

Abstract: Provided is a technique for efficiently predicting the number of occurrences of emergency medical service requests in a target area. At least patient attributes being attributes of patients, date/time information indicating dates and times of occurrences of emergency medical service requests, position information indicating places of the occurrences of the emergency medical service requests, and illness/injury information indicating illnesses and injuries that caused the emergency medical service requests are obtained. The illnesses and injuries are categorized into illness/injury groups, on a basis of a likelihood of occurrences of the illnesses and injuries in the illness/injury information obtained by bringing the patient attributes, the position information, and the date/time information into association with one another.