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首页> 外文会议>International Conference on Digital Image Processing >Fully Convolutional Networks with Double-label for Esophageal Cancer Image Segmentation by Self-Transfer Learning
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Fully Convolutional Networks with Double-label for Esophageal Cancer Image Segmentation by Self-Transfer Learning

机译：通过自我转移学习的双标签全卷积网络进行食管癌图像分割

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Cancer recognition is the prerequisite to determine appropriate treatment. This paper focuses on the semantic segmentation task of microvascular morphological types on narrowband images to aid clinical exammation of esophageal cancer. The most challenge for semantic segmentation is incomplete-labeling. Our key insight is to build fully convolutional networks (FCNs) with double-label to make pixel-wise predictions. The roi-label indicating ROIs (region of interest) is introduced as extra constraint to guild feature learning. Trained end-to-end, the FCN model with two target jointly optimizes both segmentation of sem-label (semantic label) and segmentation of roi-label within the framework of self-transfer learning based on multi-task learning theory. The learning representation ability of shared convolutional networks for sem-label is improved with support of roi-label via achieving a better understanding of information outside the ROIs. Our best FCN model gives satisfactory segmentation result with mean IU up to 77.8% (pixel accuracy > 90%). The results show that the proposed approach is able to assist clinical diagnosis to a certain extent.
机译：癌症识别是确定适当治疗的先决条件。本文重点研究窄带图像上微血管形态类型的语义分割任务，以协助食道癌的临床检查。语义分割的最大挑战是不完整标签。我们的主要见解是建立具有双标签的全卷积网络（FCN），以进行逐像素预测。指示ROI（感兴趣区域）的roi标签被引入到行会特征学习的额外约束中。经过端到端训练的具有两个目标的FCN模型在基于多任务学习理论的自我转移学习框架内共同优化sem标签（语义标签）的分割和roi标签的分割。通过对roi-label的支持，共享卷积网络对sem-label的学习表示能力得到了改善，从而可以更好地了解ROI之外的信息。我们最好的FCN模型给出令人满意的分割结果，平均IU高达77.8％（像素精度> 90％）。结果表明，所提出的方法在一定程度上可以辅助临床诊断。

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