我们第一次将深度度量学习应用于微观图像上分类塑料传染媒体壳的ProB-LEM。该物种识别任务是重建过去气候的重要信息源和科学柱子。所有Foraminifer CNN识别管道在文献中产生的黑匣子分类器缺乏人类专家的可视化选项，不能应用于开放的设定问题。这里，我们对这些管道进行基准度学习，产生表型塑料综合体形态空间的第一个科学可视化，并证明公制学习可用于在训练期间进行群体看不见。我们展示了在该域中的所有已发布的基于CNN的最新的基于CNN的最先进的基准。我们评估了我们在35个现代综合素粉末类别的45张无尽的福特公共图书馆的34,640专家注释图像上的方法。我们对此数据的结果显示，在培训中从未遇到的聚类物种在从未遇到过66.5％的精度（0.70 f1-score）中，在再现专家标签中发出92％的精度（0.84 f1分）。我们得出结论，度量学习对该领域非常有效，并作为对微泡沫识别专家自动化自动化的重要工具。用本文发布了关键代码，网络权重和数据分离，以满足全重复性。

The use of needles to access sites within organs is fundamental to many interventional medical procedures both for diagnosis and treatment. Safe and accurate navigation of a needle through living tissue to an intra-tissue target is currently often challenging or infeasible due to the presence of anatomical obstacles in the tissue, high levels of uncertainty, and natural tissue motion (e.g., due to breathing). Medical robots capable of automating needle-based procedures in vivo have the potential to overcome these challenges and enable an enhanced level of patient care and safety. In this paper, we show the first medical robot that autonomously navigates a needle inside living tissue around anatomical obstacles to an intra-tissue target. Our system leverages an aiming device and a laser-patterned highly flexible steerable needle, a type of needle capable of maneuvering along curvilinear trajectories to avoid obstacles. The autonomous robot accounts for anatomical obstacles and uncertainty in living tissue/needle interaction with replanning and control and accounts for respiratory motion by defining safe insertion time windows during the breathing cycle. We apply the system to lung biopsy, which is critical in the diagnosis of lung cancer, the leading cause of cancer-related death in the United States. We demonstrate successful performance of our system in multiple in vivo porcine studies and also demonstrate that our approach leveraging autonomous needle steering outperforms a standard manual clinical technique for lung nodule access.

这项研究中我们的主要目标是提出一种基于转移学习的方法，用于从计算机断层扫描（CT）图像中检测。用于任务的转移学习模型是验证的X受感受模型。使用了模型结构和ImageNet上的预训练权重。通过128批量的大小和224x224、3个通道输入图像训练所得的修改模型，并从原始512x512，灰度图像转换。使用的数据集是A COV19-CT-DB。数据集中的标签包括1919年COVID-1919检测的COVID-19病例和非旋转19例。首先，使用数据集的验证分区以及精确召回和宏F1分数的准确性和损失来衡量所提出方法的性能。验证集中的最终宏F1得分超过了基线模型。