针对肺部CT图像分割，提出改进UNet++框架，有效缓解小样本过拟合问题。
Framework for lung CT image segmentation based on UNet++
- 融合数据增强与优化网络结构，构建全流程分割框架
- 在小样本下达到98.03%准确率，过拟合程度最低
- 专为肺部切片CT设计，适合医疗影像实际应用
近期医学图像分割的主流模型是U-Net及其变体。尽管这些网络取得了显著成果，却忽视了医学分割领域面临的实际问题：过拟合与小样本数据。过于复杂的深度神经网络会无意义地提取冗余信息，且多数不适用于肺部切片CT图像分割任务。为克服上述局限，我们提出一种融合先进UNet++的全流程网络。该网络包含三个核心模块：数据增强、优化神经网络与参数微调。通过多种方法结合，训练结果显著优于同类工作，在小样本条件下实现98.03%的领先准确率，且过拟合程度最低。本方法是少数专门针对肺部切片CT图像分割的尝试之一。
原文摘要 · Abstract (English)
Recently, the state-of-art models for medical image segmentation is U-Net and their variants. These networks, though succeeding in deriving notable results, ignore the practical problem hanging over the medical segmentation field: overfitting and small dataset. The over-complicated deep neural networks unnecessarily extract meaningless information, and a majority of them are not suitable for lung slice CT image segmentation task. To overcome the two limitations, we proposed a new whole-process network merging advanced UNet++ model. The network comprises three main modules: data augmentation, optimized neural network, parameter fine-tuning. By incorporating diverse methods, the training results demonstrate a significant advantage over similar works, achieving leading accuracy of 98.03% with the lowest overfitting. potential. Our network is remarkable as one of the first to target on lung slice CT images.
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