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LACFormer: Toward accurate and efficient polyp segmentation

Polyp segmentation is an essential task in medical image analysis for early detection of colorectal cancer. Deep learning models, particularly encoder-decoder architectures, have been successful in polyp segmentation. However, these models often struggle to capture long-range dependencies and exhibit limited performance on small polyps. In this paper, we propose LACFormer, a novel hierarchical Transformer-CNN model incorporating the Laplacian pyramid for polyp segmentation. The proposed model combines the strengths of Transformers and CNNs along with Laplacian images to overcome the limitations of previous models. Specifically, the hierarchical Transformer backbone captures long-range dependencies and hierarchically processes the features to generate multi-scale representations. These representations are then fused with a novel CNN decoder, which enhances feature representations and refines the segmentation masks. Besides, many novel modules for effective polyp segmentation are also proposed. We evaluated our model on five popular benchmark datasets for polyp segmentation, including Kvasir, CVC-Clinic DB, CVC-ColonDB, CVC-T, and ETIS-Larib. Experimental results show that LACFormer outperforms state-of-the-art models, achieving a Dice similarity coefficient (DSC) of 0.927 and a mean intersection-over-union (mIoU) of 0.878 on CVC-ClinicDB, a DSC of 0.831 and mIoU of 0.753 on CVC-ColonDB and a DSC of 0.824 and mIoU of 0.753 on ETIS-Larib.

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