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A Multi-Scale Structure with Improved Reverse Attention for Polyp Segmentation
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DOI：10.3390/math13233794.png)
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Colorectal cancer (CRC) is the second most common global malignancy with high mortality, and timely early polyp detection is critical to halt its progression. Yet, polyp image segmentation-an essential tool-faces challenges: blurred edges, small sizes, and artifacts from intestinal folds, bubbles, and mucus. To address these, we proposed a novel segmentation model with multi-scale feature extraction. Its encoder uses Multiscale Attention-based Pyramid Vision Transformer v2 (PVTv2) for hierarchical features (lower-stage modules expand receptive field), while the decoder adopts a Parallel Multi-level Aggregation structure, plus multi-branch and improved reverse attention modules. Ablation experiments validated key modules. Compared to nine state-of-the-art networks across five benchmarks, the model showed superiority: optimal mDice/mIoU on polyp datasets, 0.2% higher mDice than MEGANet on Kvasir-SEG, and outperformance over UHA-Net and CSCA-U-Net on CVC-ClinicDB.
Keywords:
polyp segmentation
pyramid vision transformer
multi-scale feature
colonoscopy
computer vision
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