Attention Deeplabv3+: Multi-level Context Attention Mechanism for Skin Lesion Segmentation

Reza Azad, Maryam Asadi-Aghbolaghi, Mahmood Fathy, Sergio Escalera

TL;DR: In this paper, we propose Attention Deeplabv3+, an extended version of Deeplabv3+ for skin lesion segmentation by employing the idea of attention mechanism in two stages.

Abstract: Skin lesion segmentation is a challenging task due to the large variation of anatomy across different cases. In the last few years, deep learning frameworks have shown high performance in image segmentation. In this paper, we propose Attention Deeplabv3+, an extended version of Deeplabv3+ for skin lesion segmentation by employing the idea of attention mechanism in two stages. We first capture the relationship between the channels of a set of feature maps by assigning a weight for each channel (i.e., channels attention). Channel attention allows the network to emphasize more on the informative and meaningful channels by a context gating mechanism. We also exploit the second level attention strategy to integrate different layers of the atrous convolution. It helps the network to focus on the more relevant field of view to the target. The proposed model is evaluated on three datasets ISIC 2017, ISIC 2018, and $PH^2$, achieving state-of-the-art performance.

Keywords: Medical Image Segmentation, Deeplabv3+, Attention Mechanism