基于机器人的机器学习方法利用局部表面触觉成像诊断胃癌息肉
Robot-Enabled Machine Learning-Based Diagnosis of Gastric Cancer Polyps Using Partial Surface Tactile Imaging
AI总结：
提出结合七自由度机器人与VTS触觉传感器自动采集AGC肿瘤纹理数据，并用合成数据训练ML分类模型，以缓解数据稀缺与偏差并在局部接触条件下实现有效诊断。
摘要：
本文旨在共同解决晚期胃癌（AGC）肿瘤内镜诊断中现存的局限性，首次提出：（i）使用并评估我们近期开发的基于视觉的触觉传感器（VTS），以及（ii）一种互补的机器学习（ML）算法，利用肿瘤的纹理特征对其进行分类。借助一台七自由度机械臂，以及独特的、定制设计并增材制造的逼真AGC肿瘤模型，我们展示了使用VTS自动采集数据的优势，解决了传统基于ML的方法所面临的数据稀缺和偏差问题。我们在混合形态特征和传感器局部接触的条件下，采用多种统计指标，对基于合成数据训练的ML模型进行了成功评估，并与传统ML模型进行了比较。
英文摘要：
In this paper, to collectively address the existing limitations on endoscopic diagnosis of Advanced Gastric Cancer (AGC) Tumors, for the first time, we propose (i) utilization and evaluation of our recently developed Vision-based Tactile Sensor (VTS), and (ii) a complementary Machine Learning (ML) algorithm for classifying tumors using their textural features. Leveraging a seven DoF robotic manipulator and unique custom-designed and additively-manufactured realistic AGC tumor phantoms, we demonstrated the advantages of automated data collection using the VTS addressing the problem of data scarcity and biases encountered in traditional ML-based approaches. Our synthetic-data-trained ML model was successfully evaluated and compared with traditional ML models utilizing various statistical metrics even under mixed morphological characteristics and partial sensor contact.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
