论文标题
使用高灵敏的触觉传感器和扩张的残留网络对结直肠癌息肉的Pit-Pattern分类
Pit-Pattern Classification of Colorectal Cancer Polyps Using a Hyper Sensitive Vision-Based Tactile Sensor and Dilated Residual Networks
论文作者
论文摘要
在这项研究中，为了降低结直肠癌（CRC）息肉的早期检测失误率，我们建议利用一种新型的高度敏感的基于视觉的触觉传感器和一种互补和新颖的机器学习（ML）结构，以探索利用散布量的量表的构造和图像量表的范围，探索了缩放量和图像的构图的潜在，并构建了一个小型构建概念。提出的触觉传感器提供了CRC息肉的高分辨率3D纹理图像，这些图像将通过提议的扩张残留网络用于精确分类。为了收集CRC息肉的现实表面模式，用于训练ML模型并评估其性能，我们首先设计并加性地制造了160个独特的逼真的息肉幻影，由4种不同的硬度组成。接下来，将所提出的体系结构与最先进的ML模型（例如Alexnet和Densenet）进行了比较，并在性能和复杂性方面被证明是优越的。
In this study, with the goal of reducing the early detection miss rate of colorectal cancer (CRC) polyps, we propose utilizing a novel hyper-sensitive vision-based tactile sensor called HySenSe and a complementary and novel machine learning (ML) architecture that explores the potentials of utilizing dilated convolutions, the beneficial features of the ResNet architecture, and the transfer learning concept applied on a small dataset with the scale of hundreds of images. The proposed tactile sensor provides high-resolution 3D textural images of CRC polyps that will be used for their accurate classification via the proposed dilated residual network. To collect realistic surface patterns of CRC polyps for training the ML models and evaluating their performance, we first designed and additively manufactured 160 unique realistic polyp phantoms consisting of 4 different hardness. Next, the proposed architecture was compared with the state-of-the-art ML models (e.g., AlexNet and DenseNet) and proved to be superior in terms of performance and complexity.
