This study investigates barely-supervised medical image segmentation where
only few labeled data, i.e., single-digit cases are available. We observe the
key limitation of the existing state-of-the-art semi-supervised solution cross
pseudo supervision is the unsatisfactory precision of foreground classes,
leading to a degenerated result under barely-supervised learning. In this
paper, we propose a novel Compete-to-Win method (ComWin) to enhance the pseudo
label quality. In contrast to directly using one model's predictions as pseudo
labels, our key idea is that high-quality pseudo labels should be generated by
comparing multiple confidence maps produced by different networks to select the
most confident one (a compete-to-win strategy). To further refine pseudo labels
at near-boundary areas, an enhanced version of ComWin, namely, ComWin+, is
proposed by integrating a boundary-aware enhancement module. Experiments show
that our method can achieve the best performance on three public medical image
datasets for cardiac structure segmentation, pancreas segmentation and colon
tumor segmentation, respectively. The source code is now available at
this https URL

本文提出了一种新的 Compete-to-Win 方法（ComWin）来增强伪标签的质量，通过比较不同网络产生的多个置信度图并选择最有信心的一个来产生高质量伪标签，并结合边界感知增强模块在接近边界的区域进一步优化伪标签，该方法在三个公共医学图像数据集上实验表明具有最佳性能。

竞争赢取胜利：增强半监督医学图像分割的伪标签

Compete to Win: Enhancing Pseudo Labels for Barely-supervised Medical  Image Segmentation

This paper tackles the problem of semi-supervised learning when the set of
labeled samples is limited to a small number of images per class, typically
less than 10, problem that we refer to as barely-supervised learning. We
analyze in depth the behavior of a state-of-the-art semi-supervised method,
FixMatch, which relies on a weakly-augmented version of an image to obtain
supervision signal for a more strongly-augmented version. We show that it
frequently fails in barely-supervised scenarios, due to a lack of training
signal when no pseudo-label can be predicted with high confidence. We propose a
method to leverage self-supervised methods that provides training signal in the
absence of confident pseudo-labels. We then propose two methods to refine the
pseudo-label selection process which lead to further improvements. The first
one relies on a per-sample history of the model predictions, akin to a voting
scheme. The second iteratively updates class-dependent confidence thresholds to
better explore classes that are under-represented in the pseudo-labels. Our
experiments show that our approach performs significantly better on STL-10 in
the barely-supervised regime, e.g. with 4 or 8 labeled images per class.

本文针对有限标注信息下的半监督学习进行研究，分析了当前应用最广的半监督学习方法 FixMatch 在这种情况下的表现和局限，提出了一种利用自监督学习方法提供训练信号以及优化伪标签筛选过程的方案，并在 STL-10 数据集上得到了显著提高。