Abstract: An example system includes a processor to train a convolutional neural network (CNN) to detect features, and train fully connected layers of the CNN to map detected features to semantic descriptors, based on a data set including one or more lesions. The processor is to also receive a medical image to be analyzed for lesions. The processor is to further extract feature maps from the medical image using the trained CNN. The processor is also to detect a region of interest via the trained CNN and generate a bounding box around the detected region of interest. The processor is to reduce a dimension of the region of interest based on the feature maps. The processor is to generate a semantic description of the region of interest via the trained fully connected layers.

Abstract: An example system includes a processor to train a convolutional neural network (CNN) to detect features, and train fully connected layers of the CNN to map detected features to semantic descriptors, based on a data set including one or more lesions. The processor is to also receive a medical image to be analyzed for lesions. The processor is to further extract feature maps from the medical image using the trained CNN. The processor is also to detect a region of interest via the trained CNN and generate a bounding box around the detected region of interest. The processor is to reduce a dimension of the region of interest based on the feature maps. The processor is to generate a semantic description of the region of interest via the trained fully connected layers.

Abstract: Embodiments may provide methods by which fusion of information may be applied to uncertainty reduction in the results of classifier-based data analysis. For example, a method for data analysis may comprise generating a plurality of sets of data samples, each set of data samples representing a portion of input data at plurality of scales, each data sample in a set may represent the portion of the input data at a different scale and location, generating a feature map from each data sample of at least one set of data samples by learning and aggregating features using a first multi-layer convolutional processing, each data sample may be processed with multi-layer convolutional processing separately from other data samples, and generating a feature map by combining the feature maps from the data samples of each set of data samples by performing multiple-scale-multiple-location label fusion using a second multi-layer convolutional processing.