Colorectal cancer remains the third most common cancer worldwide, with over 1.9 million diagnosed cases and more than 930,000 deaths in 2020 alone. A critical challenge lies in detecting precancerous colorectal polyps, which vary greatly in size, shape, and appearance. During colonoscopy, even experienced physicians have a miss rate as high as 27% for small polyps.
To address this, a research team led by LI Hailong and LIU Guohua from Donghua University, together with ZHAO Meng from Yanshan University, proposed an improved YOLO-based model named EF-YOLO. The model incorporates several key innovations:
- Advanced multi-scale aggregation (AMSA): replaces the traditional spatial pyramid pooling module to better capture polyps of different sizes.
- Deformable convolutional network-MaxPool (DCN-MP): adaptively samples irregular polyp shapes, preserving critical morphological features.
- Transformer encoder: extracts global contextual information, especially beneficial for small or ambiguous polyps.
- Coordinate attention (CA): enhances focus on polyp regions by integrating positional and channel information.