Abstract: Provided are an image processing apparatus, an image processing method, and a program that can suppress an error in the segmentation of a medical image. An image processing apparatus includes: a segmentation unit (42) that applies deep learning to perform segmentation which classifies a medical image (200) into a specific class on the basis of a local feature of the medical image; and a global feature classification unit (46) that applies deep learning to classify the medical image into a global feature which is an overall feature of the medical image. The segmentation unit shares a weight of a first low-order layer which is a low-order layer with a second low-order layer which is a low-order layer in the global feature classification unit.

Abstract: An information processing apparatus includes one or more processors, and one or more storage devices that store a program including an image generation model trained to generate, from a first image, a second image that imitates an image obtained by an imaging protocol different from an imaging protocol of the first image. The image generation model is a model trained, through machine learning using training data in which a training image captured by a first imaging protocol is associated with a correct answer clinical parameter calculated from a corresponding image captured by a second imaging protocol different from the first imaging protocol for the same subject as the training image using a modality of the same type as a modality used to capture the training image, such that a clinical parameter calculated from a generation image output by the image generation model approaches the correct answer clinical parameter.

Abstract: Provided are a machine learning device and a method capable of performing machine learning of labeling for accurately attaching a plurality of labels to volume data at once by using learning data with mixed inconsistent labeling. A neural network (14) receives an input of multi-slice images of learning data Di (i=1, 2, . . . n) of which a class to be labeled is n types, and creates a prediction mask of n anatomical structures i by a convolutional neural network (CNN) or the like (S1). A machine learning unit (13) calculates a prediction accuracy acc(i) of the class corresponding to the learning data Di for each learning data Di (S2). The machine learning unit (13) calculates a weighted average M of an error di between the prediction accuracy acc(i) and a ground truth mask Gi. The machine learning unit (13) calculates a learning loss by a loss function Loss (S4).

Abstract: 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.

Abstract: A learning unit derives, from a target image including at least one tubular structure, in a case where an image for learning and ground-truth data of a graph structure included in the image for learning are input to an extraction model which extracts a feature vector of a plurality of nodes constituting a graph structure of the tubular structure, a loss between nodes on the graph structure included in the image for learning on the basis of an error between a feature vector distance between nodes belonging to the same graph structure and a topological distance which is a distance on a route of the graph structure between the nodes, and performs learning of the extraction model on the basis of the loss.

Abstract: A first learning unit performs learning of a first neural network that extracts a feature vector in each pixel of a target image including a plurality of objects and that outputs a feature map in which feature vectors of pixels belonging to individual objects included in the target image are clustered and distributed as a plurality of the feature vector groups in a feature space which is a space of the feature vector. A second learning unit performs learning of a second neural network that outputs a class classification result of a plurality of objects belonging to the same category included in the target image in response to input of the feature vector of the target image.

Abstract: Provided are an image processing apparatus, an image processing method, and a program that can suppress an error in the segmentation of a medical image. An image processing apparatus includes: a segmentation unit (42) that applies deep learning to perform segmentation which classifies a medical image (200) into a specific class on the basis of a local feature of the medical image; and a global feature classification unit (46) that applies deep learning to classify the medical image into a global feature which is an overall feature of the medical image. The segmentation unit shares a weight of a first low-order layer which is a low-order layer with a second low-order layer which is a low-order layer in the global feature classification unit.

Abstract: Provided are an image processing apparatus, an image processing method, and a program that can reduce the time and effort required to correct the segmentation of a medical image. An image processing apparatus includes: an image acquisition unit (40) that acquires a medical image (200); a segmentation unit (42) that performs segmentation on the medical image acquired by the image acquisition unit and classifies the medical image into prescribed classes for each local region; a global feature acquisition unit (46) that acquires a global feature indicating an overall feature of the medical image; and a correction unit (44) that corrects a class of a correction target region that is a local region whose class is to be corrected in the medical image according to the global feature with reference to a relationship between the global feature and the class.

Abstract: Provided are a machine learning device and a method capable of performing machine learning of labeling for accurately attaching a plurality of labels to volume data at once by using learning data with mixed inconsistent labeling. A neural network (14) receives an input of multi-slice images of learning data Di (i=1, 2, . . . n) of which a class to be labeled is n types, and creates a prediction mask of n anatomical structures i by a convolutional neural network (CNN) or the like (S1). A machine learning unit (13) calculates a prediction accuracy acc(i) of the class corresponding to the learning data Di for each learning data Di (S2). The machine learning unit (13) calculates a weighted average M of an error di between the prediction accuracy acc(i) and a ground truth mask Gi. The machine learning unit (13) calculates a learning loss by a loss function Loss (S4).

Abstract: 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.

Abstract: Provided is a machine learning device and method that enables machine learning of labeling, in which a plurality of labels are attached to volume data at one effort with excellent accuracy, using training data having label attachment mixed therein. A probability calculation unit (14) calculates a value (soft label) indicating a likelihood of labeling of a class Ci for each voxel of a second slice image by means of a learned teacher model (13a). A detection unit (15) detects “bronchus” and “blood vessel” for the voxels of the second slice image using a known method, such as a region expansion method and performs labeling of “bronchus” and “blood vessel”. A correction probability setting unit (16) replaces the soft label with a hard label of “bronchus” or “blood vessel” detected by the detection unit (15). A distillation unit (17) performs distillation of a student model (18a) from the teacher model (13a) using the soft label after correction by means of the correction probability setting unit (16).

Abstract: Provided herein is a system for optimizing the fed-batch hydrolysis of biomass.

Abstract: Provided herein is a system for optimizing the fed-batch hydrolysis of biomass.