FAT-Net: Feature adaptive transformers for automated skin lesion segmentation

Skin lesion segmentation from dermoscopic image is essential for improving the quantitative analysis of melanoma. However, it is still a challenging task due to the large scale variations and irregular shapes of the skin lesions. In addition, the blurred lesion boundaries between the skin lesions and the surrounding tissues may also increase the probability of incorrect segmentation. Due to the inherent limitations of traditional convolutional neural networks (CNNs) in capturing global context information, traditional CNN-based methods usually cannot achieve a satisfactory segmentation performance. In this paper, we propose a novel feature adaptive transformer network based on the classical encoder-decoder architecture, named FAT-Net, which integrates an extra transformer branch to effectively capture long-range dependencies and global context information. Furthermore, we also employ a memory-efficient decoder and a feature adaptation module to enhance the feature fusion between the adjacent-level features by activating the effective channels and restraining the irrelevant background noise. We have performed extensive experiments to verify the effectiveness of our proposed method on four public skin lesion segmentation datasets, including the ISIC 2016, ISIC 2017, ISIC 2018, and PH2 datasets. Ablation studies demonstrate the effectiveness of our feature adaptive transformers and memory-efficient strategies. Comparisons with state-of-the-art methods also verify the superiority of our proposed FAT-Net in terms of both accuracy and inference speed. The code is available at https://github.com/SZUcsh/FAT-Net.