  X-ray ptychography allows for large fields to be imaged at high resolution at
the cost of additional computational expense due to the large volume of data.
Given limited information regarding the object, the acquired data often has an
excessive amount of information that is outside the region of interest (RoI).
In this work we propose a physics-inspired unsupervised learning algorithm to
identify the RoI of an object using only diffraction patterns from a
ptychography dataset before committing computational resources to
reconstruction. Obtained diffraction patterns that are automatically identified
as not within the RoI are filtered out, allowing efficient reconstruction by
focusing only on important data within the RoI while preserving image quality.
