Metadata Improves Segmentation Through Multitasking Elicitation
Iaroslav Plutenko Mikhail Papkov Kaupo Palo Leopold Parts Dmytro Fishman
Metainformation is a common companion to biomedical images. However, this
potentially powerful additional source of signal from image acquisition has had
limited use in deep learning methods, for semantic segmentation in particular.
Here, we incorporate metadata by employing a channel modulation mechanism in
convolutional networks and study its effect on semantic segmentation tasks. We
demonstrate that metadata as additional input to a convolutional network can
improve segmentation results while being inexpensive in implementation as a
nimble add-on to popular models. We hypothesize that this benefit of metadata
can be attributed to facilitating multitask switching. This aspect of
metadata-driven systems is explored and discussed in detail.