Multi-scale aggregation networks with flexible receptive fields for melanoma segmentation. (September 2022) - Zhang, Ju; Pan, Weidong; Wang, Beng; Chen, Qing; Cheng, Yun
Abstract: Skin lesions Segmentation in dermatoscopic images is an important step in automatic diagnosis of melanoma. Due to various of shape and size in skin lesions, this is still a challenging task. In this paper, a multi-scale aggregation network with flexible receptive fields for melanoma segmentation is proposed. We propose the channel-attention dilated convolution module (CDM) to take full of context information. CDM can flexibly adjust the receptive field to capture multi-scale information in response to lesions of various shapes. In addition, we develop aggregation interaction modules to integrate features of adjacent layers of the encoder, which can reduce differences in input features of skip connection and suppress the noise from redundant information. Sub-pixel convolution is adopted as up-sampling operation for improving the fine granularity of detail features. The proposed model is trained on the ISIC 2018 skin segmentation dataset. Experiments and comparison studies are made, and demonstrate that our method produces better segmentation results than other state-of-the-art models in the evaluation metrics of accuracy (Acc), dice coefficient (Dice), Jaccard index (Jac), sensitivity (Sen) and specificity(Spe). Our method achieved 95.7% Acc, 86.4% Dice coefficient, 81.6% Jac, 91.5% Sen, 96.7% Spe, and could well adapt to the scale changes of lesions. Highlights: A multi-scale aggregation network with flexible receptive fields for melanoma segmentation is proposed. Channel-attention dilated convolution module is proposed to flexibly adjust the receptive field to capture multi-scale information. An aggregation interaction module is developed to integrate features of adjacent layers of the encoder to suppress the noise from redundant information. Sub-pixel convolution is adopted as up-sampling operation for improving the fine granularity of detailed features. Experiments and comparison studies are made, demonstrating better segmentation results than other state-of-the-art models.