DaFoEs：面向微创机器人手术中视觉-状态深度学习力估计泛化的数据集混合
DaFoEs: Mixing Datasets towards the generalization of vision-state deep-learning Force Estimation in Minimally Invasive Robotic Surgery
AI总结：
针对微创机器人手术无传感器接触力估计的数据稀缺与跨域泛化难题，本文构建 DaFoEs 视觉-触觉数据集，提出混合数据集训练流水线和可变编码器-解码器架构，显著降低跨域力估计误差。
摘要：
在微创机器人手术（MIRS）的安全交互过程中精确确定接触力仍是一个开放的研究挑战。受手术视频术后定性分析的启发，使用跨模态数据驱动的深度神经网络模型已成为预测无传感器力趋势的最新方法之一。然而，这些方法需要目前尚不可获得的大规模、多样化数据集。本文提出了一个新的视觉-触觉数据集 DaFoEs，其中包含多种柔软环境，用于训练深度神经模型。为减少单一数据集带来的偏差，我们提出了一条流水线，利用一个此前经过验证且具有不同设置的数据集，将不同的视觉和状态数据输入泛化，以进行混合数据集训练。最后，我们提出了一种可变编码器-解码器架构，可使用单个输入或输入序列预测腹腔镜器械施加的力。对于输入序列，我们使用以前缀 R 命名的循环解码器，以及一种新的时间采样方式来表示器械的加速度。在训练过程中，我们证明单一数据集训练往往会过拟合于训练数据域，却难以将结果迁移到新域。相比之下，数据集混合表现出良好的迁移效果，循环模型和非循环模型的平均相对估计力误差分别为 5% 和 12%。随着可用数据量增加 150%，我们的方法还将 Transformer 在力估计方面的有效性小幅提升至最高约 15%。总之，我们证明在 MIRS 的视觉-状态力估计中混合实验设置是通向该问题通用解决方案的一种可行途径。
英文摘要：
Precisely determining the contact force during safe interaction in Minimally Invasive Robotic Surgery (MIRS) is still an open research challenge. Inspired by post-operative qualitative analysis from surgical videos, the use of cross-modality data driven deep neural network models has been one of the newest approaches to predict sensorless force trends. However, these methods required for large and variable datasets which are not currently available. In this paper, we present a new vision-haptic dataset (DaFoEs) with variable soft environments for the training of deep neural models. In order to reduce the bias from a single dataset, we present a pipeline to generalize different vision and state data inputs for mixed dataset training, using a previously validated dataset with different setup. Finally, we present a variable encoder-decoder architecture to predict the forces done by the laparoscopic tool using single input or sequence of inputs. For input sequence, we use a recurrent decoder, named with the prefix R, and a new temporal sampling to represent the acceleration of the tool. During our training, we demonstrate that single dataset training tends to overfit to the training data domain, but has difficulties on translating the results across new domains. However, dataset mixing presents a good translation with a mean relative estimated force error of 5% and 12% for the recurrent and non-recurrent models respectively. Our method, also marginally increase the effectiveness of transformers for force estimation up to a maximum of ~15%, as the volume of available data is increase by 150%. In conclusion, we demonstrate that mixing experimental set ups for vision-state force estimation in MIRS is a possible approach towards the general solution of the problem.
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