Automatic polyp segmentation models play a pivotal role in the clinical
diagnosis of gastrointestinal diseases. In previous studies, most methods
relied on fully supervised approaches, necessitating pixel-level annotations
for model training. However, the creation of pixel-level annotations is both
expensive and time-consuming, impeding the development of model generalization.
In response to this challenge, we introduce ScribblePolyp, a novel
scribble-supervised polyp segmentation framework. Unlike fully-supervised
models, ScribblePolyp only requires the annotation of two lines (scribble
labels) for each image, significantly reducing the labeling cost. Despite the
coarse nature of scribble labels, which leave a substantial portion of pixels
unlabeled, we propose a two-branch consistency alignment approach to provide
supervision for these unlabeled pixels. The first branch employs transformation
consistency alignment to narrow the gap between predictions under different
transformations of the same input image. The second branch leverages affinity
propagation to refine predictions into a soft version, extending additional
supervision to unlabeled pixels. In summary, ScribblePolyp is an efficient
model that does not rely on teacher models or moving average pseudo labels
during training. Extensive experiments on the SUN-SEG dataset underscore the
effectiveness of ScribblePolyp, achieving a Dice score of 0.8155, with the
potential for a 1.8% improvement in the Dice score through a straightforward
self-training strategy.

ScribblePolyp 是一种高效的模型，用于胃肠疾病的息肉分割，并通过一种新的 scribble-supervised 方法，使用少量的注释（涂鸦标签）来减少标注成本，并提出一种两分支一致性对齐方法来为未标注的像素提供监督。

ScribblePolyp: 基于涂鸦指导的息肉分割通过双重一致性对齐

ScribblePolyp: Scribble-Supervised Polyp Segmentation through Dual  Consistency Alignment

Medical image segmentation plays a critical role in clinical decision-making,
treatment planning, and disease monitoring. However, accurate segmentation of
medical images is challenging due to several factors, such as the lack of
high-quality annotation, imaging noise, and anatomical differences across
patients. In addition, there is still a considerable gap in performance between
the existing label-efficient methods and fully-supervised methods. To address
the above challenges, we propose ScribbleVC, a novel framework for
scribble-supervised medical image segmentation that leverages vision and class
embeddings via the multimodal information enhancement mechanism. In addition,
ScribbleVC uniformly utilizes the CNN features and Transformer features to
achieve better visual feature extraction. The proposed method combines a
scribble-based approach with a segmentation network and a class-embedding
module to produce accurate segmentation masks. We evaluate ScribbleVC on three
benchmark datasets and compare it with state-of-the-art methods. The
experimental results demonstrate that our method outperforms existing
approaches in terms of accuracy, robustness, and efficiency. The datasets and
code are released on GitHub.

通过引入多模态信息增强机制，结合基于标注线的方法和分割网络，提出了一种基于标注线监督的医学图像分割框架 (ScribbleVC)，通过统一利用 CNN 特征和 Transformer 特征来提取更好的视觉特征，并在三个基准数据集上的实验结果表明，该方法在准确性、鲁棒性和效率方面优于现有方法。