Abstract: An apparatus for generating models for identifying objects of interest from images is disclosed. The apparatus includes an input unit that receives input images, an object segmentation unit that receives the input images from the input unit and separates regions of objects of interest from a background region, and a learning unit that learns models used by the object segmentation unit. The learning unit trains new models using training data and outputs of existing models in relation to the training data, adds the trained new models to the existing model, and repeats the training of the new models and the adding of the trained new models to the existing models.

Abstract: A machine learning device with a processor and a memory, in which the processor receives an image and calculates a feature amount of an object included in the image, the processor identifies a local part from the feature amount and calculates a local identification value, the processor calculates an overall identification value using the local identification value, and the processor generates a classifier using the local identification value and the overall identification value and stores it in the memory.

Abstract: An image diagnosis support device according to the invention executes a processing of inputting an image, a processing of extracting a feature of an object from a target image to be processed, a processing of extracting a feature of a training image and creating a feature dictionary, a processing of classifying the target image based on the feature and calculating an classification value, a processing of classifying a feature similarity of the target image using the feature dictionary and calculating a feature similarity classification value, and a processing of determining presence or absence of the object and a likelihood of the object for the target image using the classification value and the feature similarity classification value.

Abstract: An image processing device includes: an image reception unit for receiving an input image; an image generation unit for generating an image for extracting a feature from the input image; a feature extraction unit for extracting a feature from the generation image generated by the image generation unit; an identification unit for identifying an object in the image using the feature output from the feature extraction unit; an output unit for outputting an identification result output from the identification unit; and a feature map generation unit for instructing the image generation unit generating a new generation image based on the feature output from the feature extraction unit, and generating a feature map indicating a feature extraction condition for the new generation image and output the generated feature map to the feature extraction unit. With this configuration, a target object in the image is identified quickly and accurately.

Abstract: Representative features which are representative features for deciding the types or states of objects are extracted, the types or states of the objects are identified on the basis of the representative features to classify the objects, and the object classification results and the representative features are output in association with an image.

Abstract: An image diagnosis method comprises a step of acquiring an image including at least one of a tissue and a cell as an element, a step of classifying, for each partial image that is a part of the image, a property of the element included in the partial image, and a step of sorting the image into any one of benign indicating that no lesion element is present, malignant indicating that a lesion element is present, and follow-up based on classification results of the plurality of partial images.

Abstract: An image diagnosis assisting apparatus according to the present invention executes: processing of inputting an image of a tissue or cell; processing of extracting a feature amount of a tissue or cell from a processing target image; processing of extracting a feature amount of a tissue or cell from an image having a component different from that of the target image; and processing of determining presence or absence of a lesion and lesion probability for each of the target images by using a plurality of the feature amounts.

Abstract: In order to perform quantitative analysis on an object in an image, it is important to accurately identify the object, but when plural objects are in contact with each other, it is potential that a target portion cannot be accurately identified. An image is segmented into a foreground region and a background region, the foreground region being a region in which an object for which quantitative information is to be calculated is shown, and the background region being a region other than the foreground region. With respect to a first object and a second object in contact with each other in the image, a contact point between the first object and the second object is detected based on a region segmentation result output by a segmentation unit.

Abstract: A computing machine includes: a storage unit that stores learning data; a learning unit that executes learning processing for generating a classifier by using the learning data; and a generation unit that generates the learning data, and the generation unit calculates a feature amount vector handled by the classifier by using the learning data stored in the storage unit, analyzes the distribution ox the learning data in a feature amount space on the basis of the feature amount vector to specify a boundary where the classification result of the classifier changes in the feature amount space, and generates new learning data by using the learning data existing in the vicinity of the boundary.

Abstract: An appropriate learning database and an appropriate classifier are to be created and used. A machine learning device includes: a processor configured to process a data sample; and a storage device configured to store a result of the process. The processor is configured to create a plurality of classifiers based on a plurality of learning databases. Each of the plurality of learning databases stores a plurality of learning data samples. The processor is configured to create an evaluation result on identification performance of each of the plurality of classifiers, and determine, based on the evaluation result, one learning database among the plurality of learning databases and a classifier to be generated based on the one learning database as a learning database and a classifier that are to be used.

Abstract: Provided is an image processing apparatus, an automatic analysis system, and an image processing method capable of determining a state of a container or a state of a collection target based on an image acquired by a single camera. The image processing apparatus includes: an image acquisition unit configured to acquire an upper image that is an image obtained by capturing an image of a container accommodating a collection target including a sample, a reagent, and a reaction solution from above; a region calculation unit configured to calculate an edge region of the container or an upper surface region of the collection target from the upper image; and a state determination unit configured to determine a state of the container or a state of the collection target based on the edge region or the upper surface region.

Abstract: A state of a sample surface is accurately determined without lowering analysis efficiency. There is provided an apparatus for determining a state of a sample to be analyzed contained in a container, in which the apparatus acquires an image of the sample, analyzes a position and a size of an object to be detected with respect to a detection range set in the image by using the image of the sample, and determines the state of the sample based on a result of the analysis.

Abstract: Reaction values indicating based on which areas in an input image a plurality of identification results representing the states of cells or tissues are judged are calculated, and differences between a plurality of reaction values are obtained to thereby calculate the degree to which cells or tissues in an area of interest likely are in each state. Thereby, each state of cells or tissues can be visualized at an accurate position in an area of interest; therefore, diagnostic assistance to pathologist becomes possible.

Abstract: A machine learning device with a processor and a memory, in which the processor receives an image and calculates a feature amount of an object included in the image, the processor identifies a local part from the feature amount and calculates a local identification value, the processor calculates an overall identification value using the local identification value, and the processor generates a classifier using the local identification value and the overall identification value and stores it in the memory.

Abstract: A diagnostic imaging assistance apparatus according to the present invention performs a process of inputting images of a tissue and a cell, a process of extracting feature amounts of a wide view and a narrow view from a target image to be processed, a process of classifying whether the target images having different views are normal or abnormal from the feature amounts, and a process of classifying a lesion likelihood using a classification result.

Abstract: A state of a sample surface is accurately determined without lowering analysis efficiency. There is provided an apparatus for determining a state of a sample to be analyzed contained in a container, in which the apparatus acquires an image of the sample, analyzes a position and a size of an object to be detected with respect to a detection range set in the image by using the image of the sample, and determines the state of the sample based on a result of the analysis.

Abstract: A standard view which satisfies recommendations defined in a guideline on acquisition of standard views is extracted at a time of using information about defined landmarks and a defined landmark geometry in the three-dimensional information. When 3D volume data on a subject is input (S301), an image processing apparatus extracts a landmark geometry including landmarks contained in the 3D volume data on the basis of a position relationship among the landmarks (S302), determines a standard view of the 3D volume data using the landmark geometry and a position relationship that satisfies recommendations in a guideline on the acquisition of the standard views (S303), and outputs the obtained standard view (S304) to display the standard view on a display.

Abstract: An image diagnosis assisting apparatus according to the present invention executes: processing of inputting an image of a tissue or cell; processing of extracting a feature amount of a tissue or cell from a processing target image; processing of extracting a feature amount of a tissue or cell from an image having a component different from that of the target image; and processing of determining presence or absence of a lesion and lesion probability for each of the target images by using a plurality of the feature amounts.

Abstract: The present invention realizes determination of tissues and cells from an image by calculating feature values of a deformed degree of the cells even in a case where the tissues or the cells have various shapes. An image diagnosis support apparatus according to the present invention executes processing of inputting an image of cells, processing of extracting the feature values of a plurality of direction components from a target image of the processing, processing of determining whether or not the image corresponds to one-classification by using the plurality of feature values, and processing of determining whether or not the determination processing is finished with respect to all of the classifications set in advance (FIG. 1).

Abstract: A diagnostic imaging assistance apparatus according to the present invention performs a process of inputting images of a tissue and a cell, a process of extracting feature amounts of a wide view and a narrow view from a target image to be processed, a process of classifying whether the target images having different views are normal or abnormal from the feature amounts, and a process of classifying a lesion likelihood using a classification result.