Automated Detection and Classification of Laryngeal Diseases Using Deep Neural Networks

项目摘要 该项目的长期目标是通过以下方式改善喉部疾病患者的护理 为办公室柔性喉镜检查开发自动诊断支持。为了实现这一目标，我们 建议开发基于神经网络的算法来检测和分类结构性喉病变， 喉镜图像。一个自动诊断工具，如在办公室喉镜，我们建议将有 几个好处：（1）它将改善生活在喉功能障碍症状的患者获得护理的机会， （2）提高喉癌的早期发现率 潜在地降低治疗的发病率，和（3）它将被证明是学生的有价值的教学工具， 住院医师首次学习解读喉镜检查。 软喉镜检查是一种常见的在办公室程序进行的耳鼻喉科医生，以评估上 患者呼吸消化道有喉功能障碍症状。在外观上的细微差别 喉部病变使耳鼻喉科医生能够区分良性病变和可疑的恶性病变。的 正确解释喉镜检查结果的专业知识和临床敏锐度需要多年的培训， 因此喉镜检查主要仅在耳鼻喉专科诊所中进行。主要目的 该项目的主要目的是开发基于神经网络的算法来检测和分类喉结构性病变。 我们的假设是，这些算法可以使用喉部图像的大数据集进行训练，以准确地 检测和分类结构性喉病变的柔性喉镜检查。为了验证这一假设，我们， 提出以下目标：（1）生成高质量的、标记的内窥镜喉图像数据集 对应于喉的正常和结构性病变，（2）开发基于位置感知的锚点 推理神经网络用于喉病变的准确检测;（3）开发自适应网络 用于包括乳头状瘤、息肉、白斑和 疑似恶性肿瘤。具有喉部疾病诊断和治疗的专业知识和计算机 视觉，包括物体检测和分类，我们的多学科团队是唯一有资格完成 这个项目

PROJECT SUMMARY The long-term goal of this project is to improve the care of patients with laryngeal disorders through development of automated diagnostic support for in-office flexible laryngoscopy. To accomplish this goal, we propose developing neural network-based algorithms to detect and classify structural laryngeal lesions in laryngoscopy images. An automated diagnostic tool for in-office laryngoscopy such as we propose will have several benefits: (1) It will improve access to care for patients with symptoms of laryngeal dysfunction living in communities with limited otolaryngology resources, (2) It will improve early detection of laryngeal cancers potentially reducing the morbidity of treatment, and (3) It will prove a valuable teaching tool for students and residents first learning to interpret laryngoscopic exams. Flexible laryngoscopy is a common in-office procedure performed by otolaryngologists to evaluate the upper aerodigestive tract in patients with symptoms of laryngeal dysfunction. Subtle differences in the appearance of laryngeal lesions enable otolaryngologists to differentiate benign lesions from suspected malignant ones. The expertise and clinical acumen to correctly interpret laryngoscopic findings requires years of training and therefore laryngoscopy is largely only performed in subspecialty otolaryngology clinics. The primary objective of this project is to develop neural network-based algorithms to detect and classify structural laryngeal lesions. Our hypothesis is that these algorithms can be trained using a large dataset of laryngeal images to accurately detect and classify structural laryngeal lesions on flexible laryngoscopic exam. To test this hypothesis, we propose the following aims: (1) Generate a dataset of high-quality, labeled endoscopic laryngeal images corresponding to normal and structural lesions of the larynx, (2) Develop a location-aware anchor-based reasoning neural network for accurate detection of laryngeal lesions, and (3) Develop an adaptive network model for classification of structural laryngeal pathologies including papilloma, polyp, leukoplakia and suspected malignancy. With expertise in the diagnosis and treatment of laryngeal disorders and computer vision, including object detection and classification, our multidisciplinary team is uniquely qualified to complete this project.