A method includes: acquiring a training data set including pieces of training data, each of the pieces including an image of a training target, first annotation data representing a rectangular region in the image, and second annotation data; training, based on the image and the first annotation data, an object detection model specifying a rectangular region including the training target; training, based on the image and the second annotation data, a neural network; and calculating a first index value related to a relationship of a pixel number, the trained estimation model and the calculated first index value being used in a determination process that determines, based on the calculated first index value and a second index value relationship between a pixel number in an output result and an estimation result, whether or not a target in a target image is normal.

In accordance with one aspect of the disclosure, an image generation system is provided. The system includes at least one processor and at least one non-transitory, computer- readable memory accessible by the processor and having instructions that, when executed by the processor, cause the processor to receive a first patient image associated with a patient, receive a second patient image associated with the patient, train an untrained model based on the first patient image and the second patient image, provide the first patient image to the model, receive a third patient image from the model, and output the third patient image to at least one of a storage system or a display.

An apparatus for analysing a head CT scan, where the head CT scan includes a plurality of cross-sections, includes a processor configured to select a first cross-section from among the plurality of cross-sections in the head CT scan; and analyse the first cross-section to determine whether the whole head CT scan was taken with contrast. The analyzing includes determining whether the first cross-section shows contrast, based on a presence of bright patches in the first cross-section, determining whether an amount of sulci in the first cross-section is below a threshold amount. In response to determining that the first cross-section shows contrast, determine that the whole head CT scan was taken with contrast, and in response to the determining that the first cross-section does not show contrast and that the amount of sulci is below the threshold amount determining that the whole head CT scan was taken without contrast.

The present invention provides a comprehensive detection device and method for a cancerous region, and belongs to the technical field of deep learning. In the present invention, a cancerous region detection network is trained for preprocessed and annotated CT image data to predict bounding box coordinates of a cancerous region and a corresponding cancer confidence score; a clinical analysis network is trained for preprocessed clinical data with a cancer risk level to predict a cancer probability value of a corresponding patient; and a predicted cancer probability value is weighted to a predicted cancer confidence score to realize a comprehensive determination of the cancerous region. The present invention can detect a cancerous region with high accuracy and high performance.

The present invention discloses a method, apparatus and system for detecting a fundus image on the basis of machine learning. The method comprises: acquiring a fundus image to be detected; classifying the entire region of the fundus image by using a first classification model to determine whether the fundus image contains a first feature; and if the fundus image does not contain any first feature, classifying a specific region in the fundus image by using at least one second classification model to determine whether the fundus image contains any second feature, wherein the saliency of the first features are greater than that of the second features.

A controller (522) for live annotation of interventional imagery includes a memory (52220) that stores software instructions and a processor (52210) that executes the software instructions. When executed by the processor (52210), the software instructions cause the controller (522) to implement a process that includes receiving (S210) interventional imagery during an intraoperative intervention and automatically analyzing (S220) the interventional imagery for detectable features. The process executed when the processor (52210) executes the software instructions also includes detecting (S230) a detectable feature and determining (S240) at add an annotation to the interventional imagery for the detectable feature. The processor further includes identifying (S250) a location for the annotation as an identified location in the interventional imagery and adding (S260) the annotation to the interventional imagery at the identified location to correspond to the detectable feature. During the intraoperative intervention, a video is output (S270) as video output based on interventional imagery and the annotation, including the annotation overlaid on the interventional imagery at the identified location.

The invention provides a method for identifying a characteristic of one or more ultrasound images, wherein each image is of a subject imaged by an ultrasound probe using an ultrasound imaging process. The method includes obtaining a manipulation signal indicative of a manipulation of the ultrasound probe during the imaging process. A portion of the manipulation signal, which indicates the manipulation of the probe during a time period, is obtained. The obtained portion is associated with one or more ultrasound images. A neural network system is then used to classify a characteristic of the one or more ultrasound images based on both the obtained portion of the manipulation signal and the one or more images themselves. Such classification comprises applying one or more convolution kernels on the obtained portion of the manipulation signal to generate a convolution output representative of the obtained portion of the manipulation signal, and classifying the convolution output to indicate the characteristic of the one or more ultrasound images associated with the obtained portion.

There are provided machine learning device and method which can prepare divided data suitable for machine learning from volume data for learning. A machine learning unit (15) calculates detection accuracy of each organ O(j,i) in a predicted mask Pj using a loss function Loss. However, the detection accuracy of the organ O(k,i) with a volume ratio A(k,i)<Th is not calculated. That is, in the predicted mask Pk, the detection accuracy of the organ O(k,i) with a volume ratio that is small to some extent is ignored. The machine learning unit (15) changes each connection load of a neural network (16) from an output layer side to an input layer side according to the loss function Loss.

A method for analyzing neuropharmacology of a drug, including the steps of providing a set of brain activity maps representing changes of a brain activity of a living species under an influence of a plurality of known drugs each consisting of a known chemical structure; clustering the set of brain activity maps to form a plurality of functional classifiers; and classifying a brain activity map associated with a chemical compound using the functional classifiers so as to predict a neuropharmacology of the chemical compound.

A medical care support device includes: an acquisition unit that acquires medical information including medical image data representing a medical image obtained by capturing a lung of a subject, breed information representing a breed of the subject, and age information representing an age of the subject when the medical image is captured; and a derivation unit that derives a degree of calcification of the lung of the subject based on the medical information acquired by the acquisition unit and a learned model learned in advance using a plurality of pieces of learning medical information including medical image data representing a medical image in which a label is assigned to a calcified portion of the lung, the breed information, and the age information.