  The computer-aided analysis of medical scans is a longstanding goal in the
medical imaging field. Currently, deep learning has became a dominant
methodology for supporting pathologists and radiologist. Deep learning
algorithms have been successfully applied to digital pathology and radiology,
nevertheless, there are still practical issues that prevent these tools to be
widely used in practice. The main obstacles are low number of available cases
and large size of images (a.k.a. the small n, large p problem in machine
learning), and a very limited access to annotation at a pixel level that can
lead to severe overfitting and large computational requirements. We propose to
handle these issues by introducing a framework that processes a medical image
as a collection of small patches using a single, shared neural network. The
final diagnosis is provided by combining scores of individual patches using a
permutation-invariant operator (combination). In machine learning community
such approach is called a multi-instance learning (MIL).
