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.

翻译：本文为系统解决现有内窥镜诊断晚期胃癌（AGC）肿瘤的局限性，首次提出：（i）利用并评估我们近期开发的视觉触觉传感器（VTS），以及（ii）一种基于纹理特征的肿瘤分类互补机器学习（ML）算法。通过采用七自由度机器人操作器及独特定制化增材制造的真实AGC肿瘤体模，我们展示了使用VTS进行自动化数据采集的优势，解决了传统基于ML方法中常见的数据稀缺与偏差问题。即使在混合形态特征及传感器部分接触条件下，我们基于合成数据训练的ML模型仍成功通过多种统计指标进行评估，并与传统ML模型进行了有效对比。