mlsanityData to 14 Oct 2026

XBound-Former: Toward Cross-scale Boundary Modeling in Transformers

arXiv 2206.00806 ↗First posted 2 Jun 2022cs.CV cs.AIcited by 6

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Abstract (arXiv)

Skin lesion segmentation from dermoscopy images is of great significance in the quantitative analysis of skin cancers, which is yet challenging even for dermatologists due to the inherent issues, i.e., considerable size, shape and color variation, and ambiguous boundaries. Recent vision transformers have shown promising performance in handling the variation through global context modeling. Still, they have not thoroughly solved the problem of ambiguous boundaries as they ignore the complementary usage of the boundary knowledge and global contexts. In this paper, we propose a novel cross-scale boundary-aware transformer, XBound-Former, to simultaneously address the variation and boundary problems of skin lesion segmentation. XBound-Former is a purely attention-based network and catches boundary knowledge via three specially designed learners. We evaluate the model on two skin lesion datasets, ISIC-2016&PH2 and ISIC-2018, where our model consistently outperforms other convolution- and transformer-based models, especially on the boundary-wise metrics. We extensively verify the generalization ability of polyp lesion segmentation that has similar characteristics, and our model can also yield significant improvement compared to the latest models.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 36 of its 57 references that are in the map. First posted on arXiv 2 Jun 2022.

Builds on

36 of 57 references.

References · 36

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24 more
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Cited by year

Citing papers per year: 2023: 1; 2024: 2; 2025: 3.

6 papers.

  1. 2024Colorectal Polyp Segmentation in the Deep Learning Era: A Comprehensive Surveycited by 5
  2. 2025Rethinking Boundary Detection in Deep Learning-Based Medical Image Segmentationcited by 2
  3. 2024EPPS: Advanced Polyp Segmentation via Edge Information Injection and Selective Feature Decouplingcited by 1
  4. 2025Minding Fuzzy Regions: A Data-driven Alternating Learning Paradigm for Stable Lesion Segmentationcited by 1
  5. 2023PnPNet: Pull-and-Push Networks for Volumetric Segmentation with Boundary Confusioncited by 0
  6. 2025ScaleFusionNet: Transformer-Guided Multi-Scale Feature Fusion for Skin Lesion Segmentationcited by 0