The present invention relates to a method and system that automatically finds, segments and measures lesions in medical images following the Response Evaluation Criteria In Solid Tumours (RECIST) protocol. More particularly, the present invention produces an augmented version of an input computed tomography (CT) scan with an added image mask for the segmentations, 3D volumetric masks and models, measurements in 2D and 3D and statistical change analyses across scans taken at different time points. According to a first aspect, there is provided a method for determining volumetric properties of one or more lesions in medical images comprising the following steps: receiving image data; determining one or more locations of one or more lesions in the image data; creating an image segmentation (i.e. mask or contour) comprising the determined one or more locations of the one or more lesions in the image data and using the image segmentation to determine a volumetric property of the lesion.

A computer-implemented clustering method is disclosed for image segmentation, social network analysis, computational biology, market research, search engine and other applications. At the heart of the method is a point-set kernel that measures the similarity between a data point and a set of data points. The method has a procedure that employs the point-set kernel to expand from a seed point to a cluster; and finally identifies all clusters in the given dataset. Applying the method for image segmentation, it identifies several segments in the image, where points in each segment have high similarity: but points in one segment have low similarity with respect to other segments. The method is both effective and efficient that enables it to deal with large scale datasets. In contrast, existing clustering methods are either efficient or effective; and even efficient ones have difficulty dealing with large scale datasets without massive parallelization.

A computer-implemented clustering method is disclosed for image segmentation, social network analysis, computational biology, market research, search engine and other applications. At the heart of the method is a point-set kernel that measures the similarity between a data point and a set of data points. The method has a procedure that employs the point-set kernel to expand from a seed point to a cluster; and finally identifies all clusters in the given dataset. Applying the method for image segmentation, it identifies several segments in the image, where points in each segment have high similarity: but points in one segment have low similarity with respect to other segments. The method is both effective and efficient that enables it to deal with large scale datasets. In contrast, existing clustering methods are either efficient or effective; and even efficient ones have difficulty dealing with large scale datasets without massive parallelization.

Differential expression of long non-coding RNAs (lncRNAs) and enhancer RNAs (eRNAs) are used to diagnose diseases including neurological diseases, inflammatory diseases, rheumatic diseases, and autoimmune diseases. Machine learning systems are used to identify lncRNAs or eRNAs having differential expression correlated with certain disease states.

Differential expression of long non-coding RNAs (lncRNAs) and enhancer RNAs (eRNAs) are used to diagnose diseases including neurological diseases, inflammatory diseases, rheumatic diseases, and autoimmune diseases. Machine learning systems are used to identify lncRNAs or eRNAs having differential expression correlated with certain disease states.

The disclosed technology includes image-based systems and methods for object tracking within an asset area. Some exemplary methods include receiving an indication of a first object entering an asset area and receiving data indicative of a plurality of captured images. The methods also include performing, by at least one processor, object classification of the first object based on one or more of the plurality of captured images. The methods further include determining a first object location of the first object based at least in part on the object classification, and outputting an indication of the first object location.

The disclosed technology includes image-based systems and methods for object tracking within an asset area. Some exemplary methods include receiving an indication of a first object entering an asset area and receiving data indicative of a plurality of captured images. The methods also include performing, by at least one processor, object classification of the first object based on one or more of the plurality of captured images. The methods further include determining a first object location of the first object based at least in part on the object classification, and outputting an indication of the first object location.

An information processing system, including: a surveillance camera that detects a plurality of obstacles in the vicinity of a specific vehicle; a first determiner that determines whether an unidentified obstacle, which is included in the plurality of obstacles and is not visible from the specific vehicle, is present based on first information regarding the plurality of obstacles detected by the surveillance camera and vehicle information indicating the specific vehicle; and a first communicator that outputs information indicating the unidentified obstacle to the specific vehicle when the first determiner determines that the unidentified obstacle is present.

An information processing system, including: a surveillance camera that detects a plurality of obstacles in the vicinity of a specific vehicle; a first determiner that determines whether an unidentified obstacle, which is included in the plurality of obstacles and is not visible from the specific vehicle, is present based on first information regarding the plurality of obstacles detected by the surveillance camera and vehicle information indicating the specific vehicle; and a first communicator that outputs information indicating the unidentified obstacle to the specific vehicle when the first determiner determines that the unidentified obstacle is present.

There is described a computer-implemented method and system for classifying images, the computer-implemented method comprising: receiving an image to be classified, generating a vector representation of the image to be classified using an image embedding method, comparing the vector representation of the image to predefined vector representations of the predefined image categories, and identifying a relevant category amongst the predefined image categories based on the comparison, the relevant category being associated with the image to be classified and outputting the relevant category.