医学影像融合视觉Transformer：带解释的喉癌筛查
Medical Imaging Fusing Vision Transformer: Laryngeal Cancer Screening with Explanation
- Magdeburg University Hospital（马格德堡大学医院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对喉癌筛查，提出应用Transformer和注意力机制分析窄带成像以区分病变，该方法有良好分类性能，F1为82.72%，准确率82.33%，且筛查结果可解释，为喉癌筛查带来转变。
AI中文摘要：
喉癌的早期及时筛查对改善临床结果至关重要。近年来，窄带成像（NBI）内窥镜检查已成为检测喉部病变的标准诊断工具。但其有效使用需要训练有素的临床医生，且该过程耗时且存在观察者间差异。在此背景下，人工智能（AI）的应用为支持临床决策提供了有前景的解决方案。我们提出应用Transformer和注意力机制来分析窄带成像并区分良性和恶性病变。结果显示其具有良好的分类性能，F1为82.72%，准确率为82.33%。此外，喉癌筛查结果对临床医生是可解释的，利用先进的分割方法（MedSAM）为临床医生提供有用的病理信息区域。所提出的融合分类和分割的方法为喉癌筛查带来了转变。
英文摘要：
Early and timely screening of laryngeal cancer is crucial for improving clinical outcomes. In recent years, NBI endoscopy has become a standard diagnostic tool for the detection of laryngeal lesions. However, its effective use requires well-trained clinicians and the procedure is time-consuming and subject to interobserver variability. In this context, the application of artificial intelligence (AI) offers a promising solution to support clinical decision-making. In this work, we proposed applying transformer and attention mechanism for analyzing the narrow band imaging and distinguish benign and malignant lesions. Results show it has good classification performance with F1 (82.72%), accuracy(82.33%). In addition, the result of laryngeal cancer screening is explainable for clinicians. The explainability is utilizing the state of art segmentation method (MedSAM) to provide the useful pathological information area for clinicians. The proposed methodology fusing classification and segmentation provides a translating on laryngeal cancer screening.
