Transformer for Polyp Detection
This work addresses polyp detection in medical imaging, but it appears incremental as it builds on existing methods without clear novel contributions.
The paper tackled polyp detection by evaluating deep learning networks, selecting DETR as a baseline and modifying training strategies to fit the dataset, but no concrete results or numbers are provided.
In recent years, as the Transformer has performed increasingly well on NLP tasks, many researchers have ported the Transformer structure to vision tasks ,bridging the gap between NLP and CV tasks. In this work, we evaluate some deep learning network for the detection track. Because the ground truth is mask, so we can try both the current detection and segmentation method. We select the DETR as our baseline through experiment. Besides, we modify the train strategy to fit the dataset.