Efficient Polyp Segmentation Via Integrity Learning
Ziqiang Chen Kang Wang Yun Liu
Accurate polyp delineation in colonoscopy is crucial for assisting in
diagnosis, guiding interventions, and treatments. However, current
deep-learning approaches fall short due to integrity deficiency, which often
manifests as missing lesion parts. This paper introduces the integrity concept
in polyp segmentation at both macro and micro levels, aiming to alleviate
integrity deficiency. Specifically, the model should distinguish entire polyps
at the macro level and identify all components within polyps at the micro
level. Our Integrity Capturing Polyp Segmentation (IC-PolypSeg) network
utilizes lightweight backbones and 3 key components for integrity ameliorating:
1) Pixel-wise feature redistribution (PFR) module captures global spatial
correlations across channels in the final semantic-rich encoder features. 2)
Cross-stage pixel-wise feature redistribution (CPFR) module dynamically fuses
high-level semantics and low-level spatial features to capture contextual
information. 3) Coarse-to-fine calibration module combines PFR and CPFR modules
to achieve precise boundary detection. Extensive experiments on 5 public
datasets demonstrate that the proposed IC-PolypSeg outperforms 8
state-of-the-art methods in terms of higher precision and significantly
improved computational efficiency with lower computational consumption.
IC-PolypSeg-EF0 employs 300 times fewer parameters than PraNet while achieving
a real-time processing speed of 235 FPS. Importantly, IC-PolypSeg reduces the
false negative ratio on five datasets, meeting clinical requirements.