Approximated Masked Global Context Network for Skin Lesion Segmentation

The number of skin cancer cases worldwide is increasing by millions every year. A large number of patients bring great pressure to the diagnosis and treatment of skin cancer, it is urgent to apply automatic segmentation techniques to skin lesions to help the diagnosis of skin lesions and the evaluation of recovery. At present, there are still challenges in automatic skin lesion segmentation, including blurring irregular lesion boundaries, low contrast between the lesion and surrounding skin, and all kinds of interference with bubbles, lights, and hairs. We found that modeling the context relationship by using the strongest consistent masked global context can focus only on the lesion region with a high degree. Based on the observation, we propose an approximated masked global context network (AMGC-Net), which firstly approximates the masked global context by constructing the approximated masked global context, and calculates the similarity between each pixel and the approximated masked global information at the spatial level to form a global context requirements gating coefficient matrix, and then captures the dependencies between channels at the channel level to improve segmentation performance. The AMGC-Net is assessed on three public skin challenge datasets: PH2, ISBI2016, and ISIC2018. It achieves state-of-the-art results when compared to some new methods in terms of sensitivity.