HARANet: a skin lesion image segmentation network based on hybrid reverse attention

Segmentation plays a crucial role in medical image analysis, enabling the identification and localization of diseases, monitoring morphological changes, and extracting discriminative features for further diagnosis. Skin cancer, a widespread global malignancy, emphasizes the importance of early diagnosis in completely eliminating malignant tumors from the body. We propose a skin lesion segmentation network based on a hybrid reverse attention mechanism to address the issues of significant individual differences, blurred edges, and inconsistent scales between diseased and normal skin in dermoscopic images. The network combines spatial attention, which combines multidimensional feature maps and adaptively adjusts the target region of the network, with channel attention that follows the self-attention mechanism to obtain clear segmentation boundaries. By utilizing cascaded reverse attention modules and employing an erasing strategy, we guide the model to actively focus on important regions. When compared with seven existing segmentation networks on three open-source datasets, our approach achieves the best performance in three out of five evaluation metrics.