mlsanityData to 14 Oct 2026

A Region of Interest Focused Triple UNet Architecture for Skin Lesion Segmentation

arXiv 2311.12581 ↗First posted 21 Nov 2023eess.IV cs.CVcited by 0

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Abstract (arXiv)

Skin lesion segmentation is of great significance for skin lesion analysis and subsequent treatment. It is still a challenging task due to the irregular and fuzzy lesion borders, and diversity of skin lesions. In this paper, we propose Triple-UNet to automatically segment skin lesions. It is an organic combination of three UNet architectures with suitable modules. In order to concatenate the first and second sub-networks more effectively, we design a region of interest enhancement module (ROIE). The ROIE enhances the target object region of the image by using the predicted score map of the first UNet. The features learned by the first UNet and the enhanced image help the second UNet obtain a better score map. Finally, the results are fine-tuned by the third UNet. We evaluate our algorithm on a publicly available dataset of skin lesion segmentation. Experiments show that Triple-UNet outperforms the state-of-the-art on skin lesion segmentation.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 32 of its 58 references that are in the map. First posted on arXiv 21 Nov 2023.

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32 of 58 references.

References · 32

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20 more
  1. 2019BCN20000: Dermoscopic Lesions in the Wildcited by 225
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  12. 2020Illumination-based Transformations Improve Skin Lesion Segmentation in Dermoscopic Imagescited by 10
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  14. 2020Joint Demosaicking and Denoising Benefits from a Two-stage Training Strategycited by 6
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  16. 2017k-Means Clustering and Ensemble of Regressions: An Algorithm for the ISIC 2017 Skin Lesion Segmentation Challengecited by 1
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  18. 2017Automatic Skin Lesion Segmentation using Semi-supervised Learning Techniquecited by 1
  19. 2018Automatic Skin Lesion Segmentation Using GrabCut in HSV Colour Spacecited by 1
  20. 2020Frequency and Spatial domain based Saliency for Pigmented Skin Lesion Segmentationcited by 1

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