In the realm of modern diagnostic technology, video capsule endoscopy (VCE)
is a standout for its high efficacy and non-invasive nature in diagnosing
various gastrointestinal (GI) conditions, including obscure bleeding.
Importantly, for the successful diagnosis and treatment of these conditions,
accurate recognition of bleeding regions in VCE images is crucial. While deep
learning-based methods have emerged as powerful tools for the automated
analysis of VCE images, they often demand large training datasets with
comprehensive annotations. Acquiring these labeled datasets tends to be
time-consuming, costly, and requires significant domain expertise. To mitigate
this issue, we have embraced a semi-supervised learning (SSL) approach for the
bleeding regions segmentation within VCE. By adopting the `Mean Teacher'
method, we construct a student U-Net equipped with an scSE attention block,
alongside a teacher model of the same architecture. These models' parameters
are alternately updated throughout the training process. We use the
Kvasir-Capsule dataset for our experiments, which encompasses various GI
bleeding conditions. Notably, we develop the segmentation annotations for this
dataset ourselves. The findings from our experiments endorse the efficacy of
the SSL-based segmentation strategy, demonstrating its capacity to reduce
reliance on large volumes of annotations for model training, without
compromising on the accuracy of identification.

通过采用半监督学习方法和深度学习技术，我们开发了一种基于视频胶囊内窥镜图像的出血区域分割策略，有效减少了对大量标注数据的依赖，同时保持了准确识别的能力。