To mitigate the radiologist's workload, computer-aided diagnosis with the capability to review and analyze medical images is gradually deployed. Deep learning-based region of interest segmentation is among the most exciting use cases. However, this paradigm is restricted in real-world clinical applications due to poor robustness and generalization. The issue is more sinister with a lack of training data. In this paper, we address the challenge from the representation learning point of view. We investigate that the collapsed representations, as one of the main reasons which caused poor robustness and generalization, could be avoided through transfer learning. Therefore, we propose a novel two-stage framework for robust generalized segmentation. In particular, an unsupervised Tile-wise AutoEncoder (T-AE) pretraining architecture is coined to learn meaningful representation for improving the generalization and robustness of the downstream tasks. Furthermore, the learned knowledge is transferred to the segmentation benchmark. Coupled with an image reconstruction network, the representation keeps to be decoded, encouraging the model to capture more semantic features. Experiments of lung segmentation on multi chest X-ray datasets are conducted. Empirically, the related experimental results demonstrate the superior generalization capability of the proposed framework on unseen domains in terms of high performance and robustness to corruption, especially under the scenario of the limited training data.
翻译:为了减轻放射科的工作量,逐渐采用了计算机辅助诊断,以审查和分析医疗图像的能力来减轻放射科的工作量。深学习为基础的兴趣分割区是最令人兴奋的使用案例之一。然而,这种模式在现实世界临床应用中受到限制,因为缺乏稳健性和概括性。由于缺乏培训数据,这个问题更加险恶。在本文中,我们从代表性学习的角度来应对挑战。我们调查,通过转移学习,可以避免造成不健全和普遍化的主要原因之一,即崩溃的表述,这是造成不健全和普遍化的主要原因之一。因此,我们提议为稳健的通用分割建立一个新型的两阶段框架。特别是,一个非超常的自动编码(T-AE)预培训架构被创建起来,以学习有意义的代表性,改进下游任务的普遍性和稳健性。此外,所学到的知识被转移到了分解基准。与图像重建网络相结合,代表性被不断解解解,鼓励采用更精致性特征的模式。在多胸X射线数据集上进行肺分割实验。在高端的X射线数据分类框架下,在高水平的模型中特别展示了高水平的模型。