论文标题
使用弱和半监督的学习和可变形的变压器进行自动化的息肉分割
Towards Automated Polyp Segmentation Using Weakly- and Semi-Supervised Learning and Deformable Transformers
论文作者
论文摘要
息肉分割是朝着结直肠癌诊断的计算机辅助诊断的关键步骤。但是，大多数息肉分割方法都需要像素的注释数据集。注释的数据集繁琐而耗时，尤其是对于必须将时间投入患者的医生而言。我们通过提出一个新的框架来解决这个问题，该框架可以仅使用弱注释的图像以及利用未标记的图像进行训练。为此，我们提出了三个想法来解决这个问题，更具体地说的是：1）一种稀疏的前景损失，抑制了误报，改善了弱点的培训，2）使用半固定级别的培训中的相同的初始化量的介绍，使用相同的初始化量的介绍，3）批量的加权损失损失，利用了相同的培训的相同的培训的培训，用于跨度的培训，适用于适用于介绍的特征，3）灵活的空间位置。 广泛的实验结果表明，我们在五个具有挑战性的数据集上的想法优于某些最先进的完全监督模型。同样，我们的框架可以用于微调自然图像分割数据集训练的模型，从而极大地提高了其息肉性能，并令人印象深刻地证明了卓越的性能与完全监督的微调。
Polyp segmentation is a crucial step towards computer-aided diagnosis of colorectal cancer. However, most of the polyp segmentation methods require pixel-wise annotated datasets. Annotated datasets are tedious and time-consuming to produce, especially for physicians who must dedicate their time to their patients. We tackle this issue by proposing a novel framework that can be trained using only weakly annotated images along with exploiting unlabeled images. To this end, we propose three ideas to address this problem, more specifically our contributions are: 1) a novel sparse foreground loss that suppresses false positives and improves weakly-supervised training, 2) a batch-wise weighted consistency loss utilizing predicted segmentation maps from identical networks trained using different initialization during semi-supervised training, 3) a deformable transformer encoder neck for feature enhancement by fusing information across levels and flexible spatial locations. Extensive experimental results demonstrate the merits of our ideas on five challenging datasets outperforming some state-of-the-art fully supervised models. Also, our framework can be utilized to fine-tune models trained on natural image segmentation datasets drastically improving their performance for polyp segmentation and impressively demonstrating superior performance to fully supervised fine-tuning.
