Pyramid-Focus-Augmentation: Medical Image Segmentation with Step-Wise Focus
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Abstract (arXiv)
Segmentation of findings in the gastrointestinal tract is a challenging but also an important task which is an important building stone for sufficient automatic decision support systems. In this work, we present our solution for the Medico 2020 task, which focused on the problem of colon polyp segmentation. We present our simple but efficient idea of using an augmentation method that uses grids in a pyramid-like manner (large to small) for segmentation. Our results show that the proposed methods work as indented and can also lead to comparable results when competing with other methods.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 14 Dec 2020.
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Citing papers per year: 2021: 2; 2022: 0; 2023: 1; 2024: 0; 2025: 1.
4 papers.
- 2021Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformerscited by 159
- 2021SinGAN-Seg: Synthetic training data generation for medical image segmentationcited by 43
- 2023Validating polyp and instrument segmentation methods in colonoscopy through Medico 2020 and MedAI 2021 Challengescited by 3
- 2025Hybrid(Transformer+CNN)-based Polyp Segmentationcited by 0
- 2025Hybrid(Transformer+CNN)-based Polyp Segmentation8 Aug 2025
- 2023Validating polyp and instrument segmentation methods in colonoscopy through Medico 2020 and MedAI 2021 Challenges30 Jul 2023
- 2021Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformers16 Aug 2021
- 2021SinGAN-Seg: Synthetic training data generation for medical image segmentation29 Jun 2021