This paper proposes a smart handheld textural sensing medical device with complementary Machine Learning (ML) algorithms to enable on-site Colorectal Cancer (CRC) polyp diagnosis and pathology of excised tumors. The proposed unique handheld edge device benefits from a unique tactile sensing module and a dual-stage machine learning algorithms (composed of a dilated residual network and a t-SNE engine) for polyp type and stiffness characterization. Solely utilizing the occlusion-free, illumination-resilient textural images captured by the proposed tactile sensor, the framework is able to sensitively and reliably identify the type and stage of CRC polyps by classifying their texture and stiffness, respectively. Moreover, the proposed handheld medical edge device benefits from internet connectivity for enabling remote digital pathology (boosting the diagnosis in operating rooms and promoting accessibility and equity in medical diagnosis).

翻译：本文提出了一种集成互补机器学习算法的智能手持纹理感知医疗设备，用于实现离体结直肠癌息肉的现场诊断与病理分析。该独特手持边缘设备采用专用触觉感知模块及双阶段机器学习算法（由膨胀残差网络和t-SNE引擎组成），可对息肉类型与硬度特征进行表征。本框架仅利用所提触觉传感器采集的无遮挡、抗光照纹理图像，即可通过分别对息肉纹理和硬度的分类，灵敏可靠地识别结直肠癌息肉的类型与分期。此外，该手持医疗边缘设备具备互联网连接功能，可支持远程数字病理分析（提升手术室诊断效率，促进医疗诊断的可及性与公平性）。