Automated Detection and Classification of Laryngeal Diseases Using Deep Neural Networks

项目总结 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 为办公室内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喉部结构性病变 喉镜检查图像。我们建议的办公室内喉镜检查的自动诊断工具将具有 几个好处：(1)它将改善居住在 耳鼻喉科资源有限的社区，(2)它将提高喉癌的早期发现 潜在地减少治疗的发病率，以及(3)它将被证明是学生和 住院医师首先学习如何解读喉镜检查。 灵活的喉镜检查是耳鼻咽喉科医生在办公室内进行的一种常见的检查程序，用于评估上喉部 呼吸道内有喉功能障碍症状的患者。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。这个 正确解释喉镜检查结果的专业知识和临床敏锐性需要多年的培训和 因此，喉镜检查在很大程度上只在耳鼻喉科专科诊所进行。首要目标是 这个项目的目的是开发基于神经网络的算法来检测和分类喉部结构性病变。 我们的假设是，这些算法可以使用喉部图像的大型数据集进行训练，以准确地 软喉镜检查对喉部结构病变进行检测和分类。为了检验这一假设，我们 提出了以下目标：(1)生成高质量的、带标签的喉镜图像数据集 对应于喉部的正常和结构性病变，(2)发展了基于位置感知的锚 推理神经网络用于准确检测喉部病变，以及(3)开发自适应网络 喉乳头状瘤、息肉、白斑和喉部结构性病变的分类模型 疑似恶性肿瘤。在喉部疾病的诊断和治疗和计算机方面具有专业知识 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的 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.