Polyp Segmentation in Colonoscopy Images Using Ensembles of U-Nets with EfficientNet and Asymmetric Similarity Loss Function

Automatic polyp detection and segmentation are highly desirable for colon screening due to polyp miss rate by physicians during colonoscopy, which is about 25% Diagnosis of polyps in colonoscopy videos is a challenging task due to variations in the size and shape of polyps. In this paper, we adapt U-net and evaluate its performance with different modern convolutional neural networks as its encoder for polyp segmentation. One of the major challenges in training networks for polyp segmentation raises when the data are unbalanced, polyp pixels are often much lower in numbers than non-polyp pixels. A trained network with unbalanced data may make predictions with high precision and low recall, being severely biased toward the non-polyp class which is particularly undesired because false negatives are more important than false positives. We propose an asymmetric similarity loss function to address this problem and achieve a much better tradeoff between precision and recall. Finally, we propose an ensemble method for further performance improvement. We evaluate the performance of well-known polyp datasets CVC-ColonDB and ETIS-Larib PolypDB. The best results are 89.13% dice, 79.77% IOU, 90.15% recall, and 86.28% precision. Our proposed method outperforms the state-of-the-art polyp segmentation methods.