S$^2$ME: Spatial-Spectral Mutual Teaching and Ensemble Learning for
  Scribble-supervised Polyp Segmentation
An Wang Mengya Xu Yang Zhang Mobarakol Islam Hongliang Ren
Fully-supervised polyp segmentation has accomplished significant triumphs
over the years in advancing the early diagnosis of colorectal cancer. However,
label-efficient solutions from weak supervision like scribbles are rarely
explored yet primarily meaningful and demanding in medical practice due to the
expensiveness and scarcity of densely-annotated polyp data. Besides, various
deployment issues, including data shifts and corruption, put forward further
requests for model generalization and robustness. To address these concerns, we
design a framework of Spatial-Spectral Dual-branch Mutual Teaching and
Entropy-guided Pseudo Label Ensemble Learning (S$^2$ME). Concretely, for the
first time in weakly-supervised medical image segmentation, we promote the
dual-branch co-teaching framework by leveraging the intrinsic complementarity
of features extracted from the spatial and spectral domains and encouraging
cross-space consistency through collaborative optimization. Furthermore, to
produce reliable mixed pseudo labels, which enhance the effectiveness of
ensemble learning, we introduce a novel adaptive pixel-wise fusion technique
based on the entropy guidance from the spatial and spectral branches. Our
strategy efficiently mitigates the deleterious effects of uncertainty and noise
present in pseudo labels and surpasses previous alternatives in terms of
efficacy. Ultimately, we formulate a holistic optimization objective to learn
from the hybrid supervision of scribbles and pseudo labels. Extensive
experiments and evaluation on four public datasets demonstrate the superiority
of our method regarding in-distribution accuracy, out-of-distribution
generalization, and robustness, highlighting its promising clinical
significance. Our code is available at https://github.com/lofrienger/S2ME.