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Abstract In medical image processing, different adaptations based on the U-Net framework like DoubleU-Net are widely used and have been considered as fundamental models. Although DoubleU-Net enhances feature extraction and context comprehension by adding extra layers to U-Net, there are still some challenges to be solved. Multi-scale regions of interest (ROIs) might cause performance decline and information loss; complex contextual information may lead to inaccuracies in capturing environment details, affecting segmentation outcomes; unclear lesion or anatomical structure boundaries result in uncertain segmentations. To tackle these issues, We introduced an improved version of DoubleU-Net, named MFADU-Net, which incorporates multi-level feature fusion and an atrous decoder for more advanced segmentation of complex medical images. Firstly, the Multi- Level Feature Fusion Block leverages feature extraction and addresses multi-scale ROI challenges through a dual attention mechanism, excelling in detail capture and contextual understanding. Secondly, the dynamic atrous decoder offers outstanding flexibility and accuracy, further enhanced by a gated attention module for key area identification. Experimental results on CVC-ClinicDB and ISIC2018 datasets demonstrate that MFADU-Net outperforms current main methods, achieving segmentation precision of 89.3% and 93.1%, respectively.The code is available at https://github.com/Zhao-Yp/MFADU-Net.
Zhao et al. (Tue,) studied this question.