MixPolyp: Integrating Mask, Box and Scribble Supervision for Enhanced Polyp Segmentation

Limited by the expensive labeling, polyp segmentation models are plagued by data shortages. To tackle this, we propose the mixed supervised polyp segmentation paradigm (MixPolyp). Unlike traditional models relying on a single type of annotation, MixPolyp combines diverse annotation types (mask, box, and scribble) within a single model, thereby expanding the range of available data and reducing labeling costs. To achieve this, MixPolyp introduces three novel supervision losses to handle various annotations: Subspace Projection loss $\left({{{\mathcal{L}}_{{\mathcal{S}}{\mathcal{P}}}}}\right)$, Binary Minimum Entropy loss $\left({{{\mathcal{L}}_{{\mathcal{B}}{\mathcal{M}}{\mathcal{E}}}}}\right)$, and Linear Regularization loss $\left({{{\mathcal{L}}_{{\mathcal{L}}{\mathcal{R}}}}}\right)$. For box annotations, ${{\mathcal{L}}_{{\mathcal{S}}{\mathcal{P}}}}$ eliminates shape inconsistencies between the prediction and the supervision. For scribble annotations, ${{\mathcal{L}}_{{\mathcal{B}}{\mathcal{M}}{\mathcal{E}}}}$ provides supervision for unlabeled pixels through minimum entropy constraint, thereby alleviating supervision sparsity. Furthermore, ${{\mathcal{L}}_{{\mathcal{L}}{\mathcal{R}}}}$ provides dense supervision by enforcing consistency among the predictions, thus reducing the non-uniqueness. These losses are independent of the model structure, making them generally applicable. They are used only during training, adding no computational cost during inference. Extensive experiments on five datasets demonstrate MixPolyp’s effectiveness.