Abstract: Systems and methods for performing one or more medical analysis tasks are provided. 1) patient data comprising mutational data of a patient and 2) an initial PPI (protein-protein interaction) graph are received. A patient-specific PPI graph for the patient is generated based on the mutational data and the initial PPI graph. One or more medical analysis tasks for the patient are performed using a machine learning based network based on the patient data and the patient-specific PPI graph. Results of the medical analysis task are output.

Abstract: Systems and methods for predicting a malignancy of one or more candidate lesions are provided. One or more input medical images of a patient are received. One or more masks of one or more candidate lesions detected in the one or more input medical images are generated. A false positive reduction score of the one or more candidate lesions is determined using a machine learning based false positive reduction model based on the one or more input medical images and the one or more masks of the one or more candidate lesions. A qualification score of the one or more candidate lesions is determined using a machine learning based qualification model based on the false positive reduction score and features extracted from the one or more input medical images. A malignancy of the one or more candidate lesions is predicted using one or more machine learning based prediction networks based on the qualification score and a set of additional clinical and demographics features.

Abstract: Systems and methods are provided for enhancing a medical image. An initial medical image having an initial field of view is received. An augmented medical image having an expanded field of view is generated using a trained machine learning model. The expanded field of view comprises the initial field of view and an augmentation region. The augmented medical image is output.

Abstract: Systems and methods are provided for enhancing a medical image. An initial medical image having an initial field of view is received. An augmented medical image having an expanded field of view is generated using a trained machine learning model. The expanded field of view comprises the initial field of view and an augmentation region. The augmented medical image is output.