论文标题
重新考虑基于FCN的基于FCN的息肉分割的转移学习
Rethinking the transfer learning for FCN based polyp segmentation in colonoscopy
论文作者
论文摘要
除了具有固有的框架形成人工制品（例如光反射和息肉类型/形状的多样性）的结肠镜检查框架的复杂性质外，公开可用的息肉细分训练数据集有限，小且不平衡。在这种情况下，由于小型数据集上过度拟合培训，使用深神经网络的自动化息肉细分仍然是一个开放的挑战。我们提出了一个简单而有效的息肉分割管道，该管道将分割（FCN）和分类（CNN）任务结合起来。我们发现互动重量转移在密集和粗视觉任务之间的有效性，从而减轻了学习过度的学习。它促使我们在分段管道中设计了一种新的培训计划。我们的方法对CVC-内型式和kvasir-seg数据集进行了评估。与内窥镜和kvasir-seg数据集的最新方法相比，它的息肉改善可取得4.34％和5.70％的息肉改善。
Besides the complex nature of colonoscopy frames with intrinsic frame formation artefacts such as light reflections and the diversity of polyp types/shapes, the publicly available polyp segmentation training datasets are limited, small and imbalanced. In this case, the automated polyp segmentation using a deep neural network remains an open challenge due to the overfitting of training on small datasets. We proposed a simple yet effective polyp segmentation pipeline that couples the segmentation (FCN) and classification (CNN) tasks. We find the effectiveness of interactive weight transfer between dense and coarse vision tasks that mitigates the overfitting in learning. And It motivates us to design a new training scheme within our segmentation pipeline. Our method is evaluated on CVC-EndoSceneStill and Kvasir-SEG datasets. It achieves 4.34% and 5.70% Polyp-IoU improvements compared to the state-of-the-art methods on the EndoSceneStill and Kvasir-SEG datasets, respectively.
