TriDE-Net: Triple-Densely Extraction Network for Precise Skin Lesion Segmentation

Abstract

Accurate skin lesion segmentation is crucial for the quantitative analysis of skin cancer. Despite the significant advancements achieved by the deep-learning methods, the segmentation of skin lesions with irregular shapes and significant size variations is still challenging. To address the problem, we propose a Triple-Densely Extraction Network (TriDE-Net) for skin lesion segmentation, aiming to heavily extract multi-scale features in the inter- and intra-feature layers. In the TriDE-Net, a Feature-Intensive Capture Module (FICM) is designed to essentially extract multi-scale features from the intra-feature layers in a dually dense manner, and FICM is densely deployed in each skip-connection path to exploit features from the inter-feature layers. Moreover, we developed a Feature Adaptive Fusion Module (FAFM) to aggregate the decoding features to obtain accurate segmentation results. Comprehensive experiments on four widely-used skin lesion datasets consistently demonstrate that our TriDE-Net outperforms the state-of-the-art methods, with the Dice coefficient improving to 93.27%.