论文标题
使用转移学习的动脉，静脉，韧带和神经的超声多级分割对域的概括的研究
A Study of Domain Generalization on Ultrasound-based Multi-Class Segmentation of Arteries, Veins, Ligaments, and Nerves Using Transfer Learning
论文作者
论文摘要
识别股骨区域中的地标对于超声（US）基于机器人引导的导管插入至关重要，并且当用不同的扫描仪成像时，它们的呈现也有所不同。因此，过去基于深度学习的方法的性能也狭义地限于培训数据分布。可以通过微调全部或部分模型来规避这一点，但是很少讨论微调的效果。在这项工作中，我们通过微调模型中的不同连续块来研究基于美国对多个类的细分，并评估来自不同扫描仪和设置的美国数据。我们提出了一种简单的方法，可以预测对看不见的数据集的概括，并在朝着域概括的同时观察微调方法之间的统计显着差异。
Identifying landmarks in the femoral area is crucial for ultrasound (US) -based robot-guided catheter insertion, and their presentation varies when imaged with different scanners. As such, the performance of past deep learning-based approaches is also narrowly limited to the training data distribution; this can be circumvented by fine-tuning all or part of the model, yet the effects of fine-tuning are seldom discussed. In this work, we study the US-based segmentation of multiple classes through transfer learning by fine-tuning different contiguous blocks within the model, and evaluating on a gamut of US data from different scanners and settings. We propose a simple method for predicting generalization on unseen datasets and observe statistically significant differences between the fine-tuning methods while working towards domain generalization.
