Stepwise Feature Fusion: Local Guides Global
This addresses the problem of overfitting in deep learning models for medical image segmentation, specifically for colorectal cancer screening, but appears incremental as it builds on existing Transformer-based methods.
The paper tackles the challenge of accurate polyp segmentation in colonoscopy images, where varying sizes and indistinct boundaries make generalization difficult, and proposes the SSFormer model, which achieves state-of-the-art performance in learning and generalization assessments.
Colonoscopy, currently the most efficient and recognized colon polyp detection technology, is necessary for early screening and prevention of colorectal cancer. However, due to the varying size and complex morphological features of colonic polyps as well as the indistinct boundary between polyps and mucosa, accurate segmentation of polyps is still challenging. Deep learning has become popular for accurate polyp segmentation tasks with excellent results. However, due to the structure of polyps image and the varying shapes of polyps, it easy for existing deep learning models to overfitting the current dataset. As a result, the model may not process unseen colonoscopy data. To address this, we propose a new State-Of-The-Art model for medical image segmentation, the SSFormer, which uses a pyramid Transformer encoder to improve the generalization ability of models. Specifically, our proposed Progressive Locality Decoder can be adapted to the pyramid Transformer backbone to emphasize local features and restrict attention dispersion. The SSFormer achieves statet-of-the-art performance in both learning and generalization assessment.