1. A system, comprising 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 comprising one or more lesions; receive a medical image to be analyzed for lesions; extract feature maps comprising detected features from the medical image using the trained CNN; detect a region of interest via the trained CNN and generate a bounding box around the detected region of interest; reduce a dimension of the region of interest based on the feature maps; and generate a semantic description of the region of interest via the trained fully connected layers.
1. A system, comprising 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 comprising one or more lesions; receive a medical image to be analyzed for lesions; extract feature maps comprising detected features from the medical image using the trained CNN; detect a region of interest via the trained CNN and generate a bounding box around the detected region of interest; reduce a dimension of the region of interest based on the feature maps; and generate a semantic description of the region of interest via the trained fully connected layers. 5. The system of claim 1 , wherein the processor is to rank the bounding box with other bounding boxes corresponding to other detected regions of interest, wherein the ranking is based on a calculated probability that each detected region of interest comprises a tumor.