Transformer for Polyp Detection

Abstract

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.

References in corpus (4)

• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• Deformable DETR: Deformable Transformers for End-to-End Object Detection
• SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers
• nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation