Transformer for Polyp Detection
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 5 of its 5 references that are in the map. First posted on arXiv 14 Oct 2021.
Builds on
5 of 5 references.
References · 5
- 2020End-to-End Object Detection with Transformerscited by 7,414
- 2020Deformable DETR: Deformable Transformers for End-to-End Object Detectioncited by 3,422
- 2021SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformerscited by 2,940
- 2018nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentationcited by 291
- 2017LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentationcited by 214
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Citing papers per year: 2022: 3.
3 papers.