  Colonoscopy is a gold standard procedure but is highly operator-dependent.
Automated polyp segmentation, a precancerous precursor, can minimize missed
rates and timely treatment of colon cancer at an early stage. Even though there
are deep learning methods developed for this task, variability in polyp size
can impact model training, thereby limiting it to the size attribute of the
majority of samples in the training dataset that may provide sub-optimal
results to differently sized polyps. In this work, we exploit size-related and
polyp number-related features in the form of text attention during training. We
introduce an auxiliary classification task to weight the text-based embedding
that allows network to learn additional feature representations that can
distinctly adapt to differently sized polyps and can adapt to cases with
multiple polyps. Our experimental results demonstrate that these added text
embeddings improve the overall performance of the model compared to
state-of-the-art segmentation methods. We explore four different datasets and
provide insights for size-specific improvements. Our proposed text-guided
attention network (TGANet) can generalize well to variable-sized polyps in
different datasets.
