# S2ME: Spatial-Spectral Mutual Teaching and Ensemble Learning for Scribble-supervised Polyp Segmentation

arXiv: https://arxiv.org/abs/2306.00451 · HTML: https://mlsanity.com/paper/2306.00451/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 31 references that are in the map.
- First posted on arXiv 1 Jun 2023.

## Builds on (most cited first, 21 of 21)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Kvasir-SEG: A Segmented Polyp Dataset](https://mlsanity.com/paper/1911.07069/) (2019)
- [Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision](https://mlsanity.com/paper/2106.01226/) (2021)
- [ScribbleSup: Scribble-Supervised Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1604.05144/) (2016)
- [Global Filter Networks for Image Classification](https://mlsanity.com/paper/2107.00645/) (2021)
- [FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning](https://mlsanity.com/paper/2205.07246/) (2022)
- [Learning in the Frequency Domain](https://mlsanity.com/paper/2002.12416/) (2020)
- [Semi-supervised Left Atrium Segmentation with Mutual Consistency Training](https://mlsanity.com/paper/2103.02911/) (2021)
- [Video Polyp Segmentation: A Deep Learning Perspective](https://mlsanity.com/paper/2203.14291/) (2022)
- [CycleMix: A Holistic Strategy for Medical Image Segmentation from Scribble Supervision](https://mlsanity.com/paper/2203.01475/) (2022)
- [Scribble-Supervised Medical Image Segmentation via Dual-Branch Network and Dynamically Mixed Pseudo Labels Supervision](https://mlsanity.com/paper/2203.02106/) (2022)
- [A multi-centre polyp detection and segmentation dataset for generalisability assessment](https://mlsanity.com/paper/2106.04463/) (2021)
- [Scribble2Label: Scribble-Supervised Cell Segmentation via Self-Generating Pseudo-Labels with Consistency](https://mlsanity.com/paper/2006.12890/) (2020)
- [Gated CRF Loss for Weakly Supervised Semantic Image Segmentation](https://mlsanity.com/paper/1906.04651/) (2019)
- [Weakly Supervised Object Localization with Multi-fold Multiple Instance Learning](https://mlsanity.com/paper/1503.00949/) (2015)
- [ShapePU: A New PU Learning Framework Regularized by Global Consistency for Scribble Supervised Cardiac Segmentation](https://mlsanity.com/paper/2206.02118/) (2022)
- [Y-Net: A Spatiospectral Dual-Encoder Networkfor Medical Image Segmentation](https://mlsanity.com/paper/2204.07613/) (2022)
- [Class-Incremental Domain Adaptation with Smoothing and Calibration for Surgical Report Generation](https://mlsanity.com/paper/2107.11091/) (2021)
- [Learning Domain Adaptation with Model Calibration for Surgical Report Generation in Robotic Surgery](https://mlsanity.com/paper/2103.17120/) (2021)
- [Rethinking Surgical Instrument Segmentation: A Background Image Can Be All You Need](https://mlsanity.com/paper/2206.11804/) (2022)
- [FFCNet: Fourier Transform-Based Frequency Learning and Complex Convolutional Network for Colon Disease Classification](https://mlsanity.com/paper/2207.01287/) (2022)

## Built on by (most cited first, 15 of 15)

- [Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2412.13742/) (2024)
- [Frontiers in Intelligent Colonoscopy](https://mlsanity.com/paper/2410.17241/) (2024)
- [A Survey on Deep Learning for Polyp Segmentation: Techniques, Challenges and Future Trends](https://mlsanity.com/paper/2311.18373/) (2023)
- [Ultrasound Nodule Segmentation Using Asymmetric Learning with Simple Clinical Annotation](https://mlsanity.com/paper/2404.14852/) (2024)
- [SimTxtSeg: Weakly-Supervised Medical Image Segmentation with Simple Text Cues](https://mlsanity.com/paper/2406.19364/) (2024)
- [Colorectal Polyp Segmentation in the Deep Learning Era: A Comprehensive Survey](https://mlsanity.com/paper/2401.11734/) (2024)
- [ProCNS: Progressive Prototype Calibration and Noise Suppression for Weakly-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2401.14074/) (2024)
- [SparseMamba-PCL: Scribble-Supervised Medical Image Segmentation via SAM-Guided Progressive Collaborative Learning](https://mlsanity.com/paper/2503.01633/) (2025)
- [QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images](https://mlsanity.com/paper/2502.10294/) (2025)
- [ScribblePolyp: Scribble-Supervised Polyp Segmentation through Dual Consistency Alignment](https://mlsanity.com/paper/2311.05122/) (2023)
- [Skeleton Supervised Airway Segmentation](https://mlsanity.com/paper/2403.06510/) (2024)
- [Weakly Supervised Segmentation Framework for Thyroid Nodule Based on High-confidence Labels and High-rationality Losses](https://mlsanity.com/paper/2502.19707/) (2025)
- [EndoControlMag: Robust Endoscopic Vascular Motion Magnification with Periodic Reference Resetting and Hierarchical Tissue-aware Dual-Mask Control](https://mlsanity.com/paper/2507.15292/) (2025)
- [S2ML: Spatio-Spectral Mutual Learning for Depth Completion](https://mlsanity.com/paper/2511.06033/) (2025)
- [Prompt-Conditioned Channel Attention for Hierarchical Feature Modulation toward Anatomy-Agnostic Segmentation](https://mlsanity.com/paper/2608.20229/) (2026)

## Abstract (arXiv)

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 (S2ME). 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.