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arXiv · 2501.10957

MARIO: A Mixed Annotation Framework For Polyp Segmentation

Haoyang Li·Yiwen Hu·Jun Wei·Zhen Li
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Abstract

Existing polyp segmentation models are limited by high labeling costs and the small size of datasets. Additionally, vast polyp datasets remain underutilized because these models typically rely on a single type of annotation. To address this dilemma, we introduce MARIO, a mixed supervision model designed to accommodate various annotation types, significantly expanding the range of usable data. MARIO learns from underutilized datasets by incorporating five forms of supervision: pixel-level, box-level, polygon-level, scribblelevel, and point-level. Each form of supervision is associated with a tailored loss that effectively leverages the supervision labels while minimizing the noise. This allows MARIO to move beyond the constraints of relying on a single annotation type. Furthermore, MARIO primarily utilizes dataset with weak and cheap annotations, reducing the dependence on large-scale, fully annotated ones. Experimental results across five benchmark datasets demonstrate that MARIO consistently outperforms existing methods, highlighting its efficacy in balancing trade-offs between different forms of supervision and maximizing polyp segmentation performance

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Haoyang Li, Yiwen Hu, Jun Wei, Zhen Li. 2025-01-19. MARIO: A Mixed Annotation Framework For Polyp Segmentation. https://arxiv.org/abs/2501.10957

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