Incorporating Metadata for Semantic Segmentation by employing Channel Attention Mechanism

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.