Abstract:
Medial image segmentation assists in computer-aided diagnosis, surgeries,
and treatment. Digitize tissue slide images are used to analyze and segment
glands, nuclei, and other biomarkers which are further used in computer aided medical applications to this end many researchers developed different
neural networks to perform segmentation on histological images. Mostly
these networks are based on encode-decoder architecture and also utilize
complex attention modules or transformers. However, these networks are less
accurate to capture relevant local and global features with accurate boundary
detection at multiple scale therefore we proposed an encoder-decoder network
with our proposed Quick Attention module and a Loss function which is a
combination of Binary Cross Entropy (BCE) Loss, Focal Loss Dice Loss.
We evaluate the generalization capability of our proposed network on two
publicly available datasets for medical image segmentation MoNuSeg and
GlaS and outperform the state-of-the-art networks with 1.99 % improvement
on the MoNuSeg dataset and 7.15 % improvement on the GlaS dataset.
