MIT bakes left-right symmetry into a model that tracks a moving fetal head
Let the head shift during imaging and each 2D image slice can end up taken from a different angle, producing gaps and odd orientations in what should read as one stacked three-dimensional picture. To help with that, researchers at MIT, Boston Children's Hospital and Inria developed and trained a deep-learning model called E(3)-Pose, which helps image the fetus's head while it is in motion so a radiologist ends up with a high-resolution image to examine. The interesting design choice is where the physics sits. Rather than training the model to learn physics from data, the left-right symmetry of the fetal head is embedded directly in the network's architecture, which makes it easier for the model to identify the brain's orientation. A prior you can state up front, in other words, instead of a pattern you hope the network stumbles onto.
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Notice how modest the prior is. Nobody taught the model anatomy, just that a head has a mirror plane. That one sentence of geometry is doing more work than another thousand scans might have, and it's the kind of constraint a person can actually argue for.
The elegant part lives inside the architecture; the awkward part lived outside it. Muthukrishnan came to the project with no physics background, and her experience was mostly in computer science, then found herself implementing the work on the scanner. Deciding that heads are symmetric is easy. Getting that decision to survive real hardware is the other job.