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Medical image segmentation assists in computer-aided diagnosis, surgeries,and treatment. Digitize tissue slide images are used to analyze and segmentglands, nuclei, and other biomarkers which are further used in computer-aidedmedical applications. To this end, many researchers developed different neuralnetworks to perform segmentation on histological images, mostly these networksare based on encoder-decoder architecture and also utilize complex attentionmodules or transformers. However, these networks are less accurate to capturerelevant local and global features with accurate boundary detection at multiplescales, therefore, we proposed an Encoder-Decoder Network, Quick AttentionModule and a Multi Loss Function (combination of Binary Cross Entropy (BCE)Loss, Focal Loss & Dice Loss). We evaluate the generalization capability of ourproposed network on two publicly available datasets for medical imagesegmentation MoNuSeg and GlaS and outperform the state-of-the-art networks with1.99% improvement on the MoNuSeg dataset and 7.15% improvement on the GlaSdataset. Implementation Code is available at this link: https://bit.ly/HistoSeg

HistoSeg: Quick attention with multi-loss function for multi-structure segmentation in digital histology images