  Medical image segmentation can provide detailed information for clinical
analysis which can be useful for scenarios where the detailed location of a
finding is important. Knowing the location of disease can play a vital role in
treatment and decision-making. Convolutional neural network (CNN) based
encoder-decoder techniques have advanced the performance of automated medical
image segmentation systems. Several such CNN-based methodologies utilize
techniques such as spatial- and channel-wise attention to enhance performance.
Another technique that has drawn attention in recent years is residual dense
blocks (RDBs). The successive convolutional layers in densely connected blocks
are capable of extracting diverse features with varied receptive fields and
thus, enhancing performance. However, consecutive stacked convolutional
operators may not necessarily generate features that facilitate the
identification of the target structures. In this paper, we propose a
progressive alternating attention network (PAANet). We develop progressive
alternating attention dense (PAAD) blocks, which construct a guiding attention
map (GAM) after every convolutional layer in the dense blocks using features
from all scales. The GAM allows the following layers in the dense blocks to
focus on the spatial locations relevant to the target region. Every alternate
PAAD block inverts the GAM to generate a reverse attention map which guides
ensuing layers to extract boundary and edge-related information, refining the
segmentation process. Our experiments on three different biomedical image
segmentation datasets exhibit that our PAANet achieves favourable performance
when compared to other state-of-the-art methods.
