Inter-Scale Dependency Modeling for Skin Lesion Segmentation with Transformer-based Networks (2310.13727v1)
Abstract: Melanoma is a dangerous form of skin cancer caused by the abnormal growth of skin cells. Fully Convolutional Network (FCN) approaches, including the U-Net architecture, can automatically segment skin lesions to aid diagnosis. The symmetrical U-Net model has shown outstanding results, but its use of a convolutional operation limits its ability to capture long-range dependencies, which are essential for accurate medical image segmentation. In addition, the U-shaped structure suffers from the semantic gaps between the encoder and decoder. In this study, we developed and evaluated a U-shaped hierarchical Transformer-based structure for skin lesion segmentation while we proposed an Inter-scale Context Fusion (ISCF) to utilize the attention correlations in each stage of the encoder to adaptively combine the contexts coming from each stage to hinder the semantic gaps. The preliminary results of the skin lesion segmentation benchmark endorse the applicability and efficacy of the ISCF module.
- Attention swin u-net: Cross-contextual attention mechanism for skin lesion segmentation. In 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), pages 1–5. IEEE, 2023.
- Medical image segmentation review: The success of u-net. arXiv preprint arXiv:2211.14830, 2022.
- Swin-unet: Unet-like pure transformer for medical image segmentation. In Computer Vision–ECCV 2022 Workshops: Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part III, pages 205–218. Springer, 2023.
- Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306, 2021.
- Skin lesion segmentation improved by transformer-based networks with inter-scale dependency modeling. In International Workshop on Machine Learning in Medical Imaging, pages 351–360. Springer, 2023.
- Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF international conference on computer vision, pages 10012–10022, 2021.
- Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999, 2018.
- U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention, pages 234–241. Springer, 2015.
- Efficient attention: Attention with linear complexities. In Proceedings of the IEEE/CVF winter conference on applications of computer vision, pages 3531–3539, 2021.
- Cancer statistics, 2022. CA: A Cancer Journal for Clinicians, 72(1):7–33, 2022.
- Anti-oversmoothing in deep vision transformers via the fourier domain analysis: From theory to practice. In International Conference on Learning Representations, 2022. URL https://openreview.net/forum?id=O476oWmiNNp.
- Fat-net: Feature adaptive transformers for automated skin lesion segmentation. Medical Image Analysis, 76:102327, 2022.
- Segformer: Simple and efficient design for semantic segmentation with transformers. Advances in Neural Information Processing Systems, 34:12077–12090, 2021.
Collections
Sign up for free to add this paper to one or more collections.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.