基于机器学习的肾结石体内识别研究
On the in vivo recognition of kidney stones using machine learning
AI总结：
本研究针对肾结石体内图像分类需求，构建输尿管镜采集的四类常见结石图像数据集，对比浅层机器学习与深度学习方法的识别性能，发现Inception v3表现最优，且合适特征可让浅层方法接近深度学习效果，为体内肾结石即时识别提供支撑。
摘要：
确定肾结石类型可帮助泌尿科医生制定治疗方案，避免肾结石复发。一种自动化的基于体内图像的分类方法，将是实现诊断第一阶段所需的肾结石类型即时识别的重要一步。现有文献已在体外数据（即场景与图像采集条件高度受控的环境下）证明，肾结石自动分类具备可行性。\n本项先导研究对比了6种浅层机器学习方法与3种深度学习架构的肾结石识别性能，测试所用的体内图像涵盖四种最常见的泌尿系结石类型，均在标准输尿管镜检查过程中通过内窥镜采集。本文详述了数据库构建流程，以及所测试的肾结石分类器的设计细节。\n尽管Inception v3架构取得了最优结果（加权精确率、召回率与F1值分别为0.97、0.98、0.97），但研究同时表明，选择合适的颜色空间与纹理特征，可使浅层机器学习方法的性能逼近表现最优的深度学习方法（XGBoost分类器的加权精确率、召回率与F1值可达0.96）。本文是首篇探索输尿管镜检查采集图像中最具判别性提取特征的研究。
英文摘要：
Determining the type of kidney stones allows urologists to prescribe a treatment to avoid recurrence of renal lithiasis. An automated in-vivo image-based classification method would be an important step towards an immediate identification of the kidney stone type required as a first phase of the diagnosis. In the literature it was shown on ex-vivo data (i.e., in very controlled scene and image acquisition conditions) that an automated kidney stone classification is indeed feasible. This pilot study compares the kidney stone recognition performances of six shallow machine learning methods and three deep-learning architectures which were tested with in-vivo images of the four most frequent urinary calculi types acquired with an endoscope during standard ureteroscopies. This contribution details the database construction and the design of the tested kidney stones classifiers. Even if the best results were obtained by the Inception v3 architecture (weighted precision, recall and F1-score of 0.97, 0.98 and 0.97, respectively), it is also shown that choosing an appropriate colour space and texture features allows a shallow machine learning method to approach closely the performances of the most promising deep-learning methods (the XGBoost classifier led to weighted precision, recall and F1-score values of 0.96). This paper is the first one that explores the most discriminant features to be extracted from images acquired during ureteroscopies.
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。
