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Skin Lesion Segmentation Improved by Transformer-based Networks with Inter-scale Dependency Modeling

20 Oct 2023arXiv:2310.13604archive 2025-07-28

Sania Eskandari, Janet Lumpp, Luis Sanchez Giraldo

Melanoma, a dangerous type of skin cancer resulting from abnormal skin cell growth, can be treated if detected early. Various approaches using Fully Convolutional Networks (FCNs) have been proposed, with the U-Net architecture being prominent To aid in its diagnosis through automatic skin lesion segmentation. However, the symmetrical U-Net model's reliance on convolutional operations hinders its ability to capture long-range dependencies crucial for accurate medical image segmentation. Several Transformer-based U-Net topologies have recently been created to overcome this limitation by replacing CNN blocks with different Transformer modules to capture local and global representations. Furthermore, the U-shaped structure is hampered by semantic gaps between the encoder and decoder. This study intends to increase the network's feature re-usability by carefully building the skip connection path. Integrating an already calculated attention affinity within the skip connection path improves the typical concatenation process utilized in the conventional skip connection path. As a result, we propose a U-shaped hierarchical Transformer-based structure for skin lesion segmentation and an Inter-scale Context Fusion (ISCF) method that uses attention correlations in each stage of the encoder to adaptively combine the contexts from each stage to mitigate semantic gaps. The findings from two skin lesion segmentation benchmarks support the ISCF module's applicability and effectiveness. The code is publicly available at \url{https://github.com/saniaesk/skin-lesion-segmentation}

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Decoder Image Segmentation Lesion Segmentation Medical Image Segmentation Segmentation Semantic Segmentation Skin Lesion Segmentation

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Absolute Position Encodings Adam Attention BPE Concatenated Skip Connection Convolution Dense Connections Dropout Label Smoothing Layer Normalization Linear Layer Max Pooling Multi-Head Attention Position-Wise Feed-Forward Layer ReLU Residual Connection Softmax Transformer U-Net

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