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

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.