Background: The size variation, complex semantic environment and high similarity in medical images often prevent deep learning models from achieving good performance.
Purpose: To overcome these problems and improve the model segmentation performance and generalizability.
Methods: We propose the key class feature reconstruction module (KCRM), which ranks channel weights and selects key features (KFs) that contribute more to the segmentation results for each class. Meanwhile, KCRM reconstructs all local features to establish the dependence relationship from local features to KFs. In addition, we propose the spatial gating module (SGM), which employs KFs to generate two spatial maps to suppress irrelevant regions, strengthening the ability to locate semantic objects. Finally, we enable the model to adapt to size variations by diversifying the receptive field.
Results: We integrate these modules into class key feature extraction and fusion network (CKFFNet) and validate its performance on three public medical datasets: CHAOS, UW-Madison, and ISIC2017. The experimental results show that our method achieves better segmentation results and generalizability than those of mainstream methods.
Conclusion: Through quantitative and qualitative research, the proposed module improves the segmentation results and enhances the model generalizability, making it suitable for application and expansion.
Keywords: feature extraction; fusion; medical images; ranking channels; semantic segmentation.
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