Transfer Learning with Human Corneal Tissues: An Analysis of Optimal Cut-Off Layer

Nadezhda Prodanova, Johannes Stegmaier, Stephan Allgeier, Sebastian Bohn, Oliver Stachs, Bernd Köhler, Ralf Mikut, Andreas Bartschat

Abstract:

Transfer learning is a powerful tool to adapt trained neural networks to new tasks.
Depending on the similarity of the original task to the new task, the selection of
the cut-off layer is critical. For medical applications like tissue classification, the
last layers of an object classification network might not be optimal. We found
that on real data of human corneal tissues the best feature representation can be
found in the middle layers of the Inception-v3 and in the rear layers of the VGG-19
architecture.

Keywords: deep learning, transfer learning, corneal confocal microscopy, tissue classification