Here we employ deep learning models using whole slide images (WSIs) of hematoxylin and eosin (H&E)-stained formalin-fixed paraffin embedded (FFPE) NSCLC tumor specimens to identify tumors most likely to harbor ALK and ROS1 fusions in a cohort of 33,014 patients, out of which 306 and 697 patients are positive for ROS1 or ALK fusions, respectively. Here, ROS1 is linked to neoplasm.