  Medical 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 encoder-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
scales, therefore, we proposed an Encoder-Decoder Network, Quick Attention
Module and a Multi Loss Function (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. Implementation Code is available at this link: https://bit.ly/HistoSeg
