论文标题
减少质地偏见可改善深度分割模型的鲁棒性
Reducing Textural Bias Improves Robustness of Deep Segmentation Models
论文作者
论文摘要
尽管深度学习取得了进步，但在域转移下的鲁棒性仍然是医学成像环境中的主要瓶颈。关于自然图像的发现表明，在执行图像分类任务时，深层神经模型可以显示出强烈的纹理偏见。在这项彻底的实证研究中，我们从关于自然图像的发现中汲取灵感，并研究解决质地偏见现象的方式，当应用于三维（3D）医学数据时，深层分割模型的稳健性。为了实现这一目标，使用开发的人类Connectome项目的公开MRI扫描用于研究模拟纹理噪声可以在复杂的语义分割任务中训练健壮模型的方法。我们贡献了一项由176个实验组成的广泛实证研究，并说明了在训练之前应用特定类型的模拟纹理噪声如何导致纹理不变模型，从而在细分扫描扫描时会改善稳健性，而扫描被以前未见的噪声类型和水平损坏。
Despite advances in deep learning, robustness under domain shift remains a major bottleneck in medical imaging settings. Findings on natural images suggest that deep neural models can show a strong textural bias when carrying out image classification tasks. In this thorough empirical study, we draw inspiration from findings on natural images and investigate ways in which addressing the textural bias phenomenon could bring up the robustness of deep segmentation models when applied to three-dimensional (3D) medical data. To achieve this, publicly available MRI scans from the Developing Human Connectome Project are used to study ways in which simulating textural noise can help train robust models in a complex semantic segmentation task. We contribute an extensive empirical investigation consisting of 176 experiments and illustrate how applying specific types of simulated textural noise prior to training can lead to texture invariant models, resulting in improved robustness when segmenting scans corrupted by previously unseen noise types and levels.
