Abstract: A system, computer-readable storage medium and method for prognosis of head and neck cancer, includes an input for receiving electronic health records (EHR) of a patient, an input for receiving multimodal images of a head and neck area of the patient, a feature extraction module for converting the electronic health records and multimodal images into at least one feature vector, a hybrid machine learning architecture that includes a multi-task logistic regression (MTLR) model and a multi-layer artificial neural network, the hybrid architecture takes as input the at least one feature vector and outputs a final risk score of prognosis for head and neck cancer for the patient.

Abstract: A system and method are provided to automatically categorize biological and medical images. The new system and method can incorporate a machine learning classifier in which novel ideas are provided to guide the classifier to focus on regions of interest (ROI) within medical images for categorizing or classifying the images. The system and method can ignore regions when misleading structures exist. The detection and classification of one or more features of interest within a discriminative region of interest within an image are rendered invariant to differences in translation, orientation and/or scaling of the one or more features of interest within the medical image(s). The system and method allow a processor to more quickly, efficiently and accurately process and categorize medical images.

Abstract: A system and method are provided to automatically categorize biological and medical images. The new system and method can incorporate a machine learning classifier in which novel ideas are provided to guide the classifier to focus on regions of interest (ROI) within medical images for categorizing or classifying the images. The system and method can ignore regions when misleading structures exist. The detection and classification of one or more features of interest within a discriminative region of interest within an image are rendered invariant to differences in translation, orientation and/or scaling of the one or more features of interest within the medical image(s). The system and method allow a processor to more quickly, efficiently and accurately process and categorize medical images.