MaMfi-Net: Multi-attention and multi-feature interaction network in skin lesion segmentation

Abstract

Segmenting skin lesions from dermoscopic images accurately and reliably is essential for disease diagnosis and treatment. However, because lesions vary in size, shape, and color, it remains difficult to detect lesion boundaries and local features. Furthermore, manually portraying lesions takes time and requires the expertise of a skilled physician. To address this issue, a segmentation network with multi-attention mechanisms and multi-feature interactions (MaMfi-Net) is proposed, which can accurately capture the main area of lesions at the encoding stage and efficiently supplement detailed information at the decoding stage to synthetically improve skin lesions' prediction capability. We conducted extensive experiments on two publicly available datasets, including ISIC-2016 and ISIC-2018. Experiment findings show that our method outperforms several cutting-edge approaches, with segmentation results that are closer to the true lesions and superior prediction ability for some challenging images. Meanwhile, the metrics have a lower margin of error.