Abstract:
Provided are an information processing device, a program, a trained model, a diagnostic support device, a learning device, and a prediction model generation method that can perform prediction with high accuracy using images. An information processing device includes: an information acquisition unit that receives an input of image data and non-image data related to a target matter; and a prediction unit that predicts an aspect related to the matter at a time different from a time when the image data is captured on the basis of the image data and the non-image data input through the information acquisition unit. The prediction unit performs weighting calculation by a calculation method, which outputs a combination of products of elements of a first feature amount calculated from the image data and a second feature amount calculated from the non-image data, to calculate a third feature amount in which the first feature amount and the second feature amount are fused and performs the prediction on the basis of the third feature amount.
Abstract:
A first image and a second image obtained by imaging the same subject with different types of modalities are obtained. The first image is deformed, and similarity between the deformed first image and the second image is evaluated by an evaluation function that evaluates correlation between distributions of corresponding pixel values of the two images to estimate an image deformation amount of the first image. Based on the estimated image deformation amount, a deformed image of the first image is generated. The evaluation function includes a term representing a measure of correlation between a pixel value of the deformed first image and a corresponding pixel value of the second image, wherein the term evaluates the correlation based on probability information that indicates a probability of each combination of corresponding pixel values of the first image and the second image.
Abstract:
A clinical trial support apparatus includes a processor, in which the processor predicts a progression speed of a target disease for subjects of a clinical trial, divides the subjects into a plurality of groups according to a prediction result of the progression speed of the target disease, and allocates the subjects to a treatment group to which a test drug is administered and a placebo group to which a placebo is administered for each of the plurality of groups.
Abstract:
An image processing apparatus includes a processor and a memory connected to or built in the processor. The processor is configured to perform non-linear registration processing on a first medical image and a second medical image among a plurality of medical images, and generate at least one new medical image that is used for training a machine learning model for the medical images by transforming at least one medical image of the first medical image or the second medical image based on a result of the non-linear registration processing.
Abstract:
In a medical image processing apparatus, a medical image processing method, and a medical image processing program, in a case where there are a plurality of past brain images, it is possible to select a past brain image with which the atrophy rate of the brain can be accurately calculated. An image acquisition unit acquires a target brain image Bt as a diagnostic target and a plurality of past brain images Bpi, which have earlier imaging dates and times than the target brain image Bt, for the same subject. A similarity calculation unit calculates the similarity between each of the plurality of past brain images Bpi and a standard brain image Bs. A selection unit selects a reference brain image B0 serving as a reference for calculating the amount of change of the brain from the plurality of past brain images Bpi.
Abstract:
A medical support device includes: a processor; and a memory connected to or built into the processor, and the processor acquires target input data which is input data related to a disease of a subject candidate for a clinical trial of a drug, and a clinical trial period, inputs the target input data and the clinical trial period to a machine learning model trained using supervised training data including accumulated input data related to a disease at two or more points in time and a time interval of the input data, and causes the machine learning model to output a prediction result regarding the disease of the subject candidate in the clinical trial period, and outputs selection reference information for determining whether or not to select the subject candidate as a subject for the clinical trial, according to the prediction result.
Abstract:
A diagnosis support device includes a processor and a memory connected to or built in the processor. The processor is configured to perform non-linear registration processing of a target image which is a medical image to be analyzed and at least one representative image generated from a plurality of medical images, input at least one of transformation amount information of the target image obtained by the non-linear registration processing or a feature amount derived from the transformation amount information to a disease opinion derivation model, and output a disease opinion from the disease opinion derivation model.
Abstract:
There is provided a medical information acquisition device including an information acquisition unit that acquires functional change information obtained on the basis of a reference image and a past image acquired by capturing images of the same subject at a reference time and a past time closer to the past than the reference time, respectively, using a trained model.
Abstract:
A brain image normalization apparatus, having a processor configured to: detect at least four reference landmarks of a left eye, a right eye, a diencephalon, a fornix, a corpus callosum, a left hippocampus, and a right hippocampus from a brain image including a brain of a subject; perform registration between the detected reference landmarks and reference landmarks corresponding to the detected reference landmarks included in a standard brain image; and normalize the brain image based on a result of the registration.
Abstract:
A first image and a second image are obtained; the amount of deformation of the first image is estimated by evaluating the degree of similarity between a deformed first image and the second image, using an evaluation function that evaluates the correlation between the distribution of corresponding pixel values within the two images; and an image, which is the first image deformed based on the estimated amount of deformation, is generated. The evaluation function evaluates the degree of similarity between the deformed first image and the second image, based on degrees of similarities of divided images that represent degrees of similarities among the distributions of pixel values of each pair of divided first images and divided second images, which respectively are images that the deformed first image is divided into and images that the second image is divided into, according to predetermined dividing conditions.