Bibliographic description:
Plutenko, Iaroslav. Incorporating Metadata for Semantic Segmentation by employing Channel Attention Mechanism / Iaroslav Plutenko; Supervisor: Dmytro Fishman; Ukrainian Catholic University, Department of Computer Sciences. – Lviv : [s.n.], 2021. – 58 p.: ill.
Abstract:
The meta-information accompanying data from image acquisition devices has limited
use in microscopy image processing techniques involving Deep Learning. This
project aims to incorporate the supplementary metadata for semantic segmentation
by employing a channel selection mechanism in convolutional networks outlining
its potential benefits and practical applications where metadata can be used
for switching tasks within a master model. The results of conducted experiments
show that meta-information is helpful, and the phenomenon is more expressed with
incompatible segmentation tasks, where a multi-head model or separate models are
required otherwise. Overall, we have achieved a slight increase in scores for similar
tasks as well and demonstrated the applicability of the CNN model for separate
tasks, forcing it to work as an ensemble, leveraging the beneficial effect of multi-task
learning.
