Medical protocol change in real-time imaging

A device comprising:

• a processor and memory configured to implement the steps of:
• receiving imaging data relating to a body part of a human patient, taken at a medical imaging facility;
• classifying by machine learning the imaging data into a normal or an abnormal class;
• if the imaging data is classified as abnormal, suggesting collection of additional imaging data of the body part while the human patient is still present at the medical imaging facility;
• for the abnormal class, spatially localizing an abnormality within the imaging data of the body part;
• categorizing by machine learning the abnormality within a category;
• and
• implement computer code of a convolutional neural network (CNN), the CNN comprising a plurality of layers in a sequence, each layer comprising a plurality of nodes, wherein:
• each node of each layer is connected to at least one other node of a subsequent or preceding layer in the sequence,
• each node accepts an input value and outputs an output value,
• the classifying, spatially localizing and categorizing are by the CNN,
• the imaging data comprises a training set of correctly diagnosed imaging data, the nodes in the CNN comprise parameters, and the CNN is trained on the training set to optimize the node parameters,
• the plurality of layers comprises convolutional layers, rectified linear unit layers, non-linear pooling layers and fully connected layers, and
• the non-linear pooling layers are max pooling layers that partition at least one image of the imaging data into non-overlapping sub-regions and output a maximum value for each sub-region.