用视觉变压器+注意力机制，自动区分喉癌良恶性病变。
Medical Imaging Fusing Vision Transformer: Laryngeal Cancer Screening with Explanation
- 结合视觉变换器与注意力机制分析窄带成像图像。
- 分类F1达82.72%，准确率82.33%，结果可解释。
- 通过MedSAM分割病灶区域，辅助医生诊断决策。
早期及时筛查喉癌对改善临床结局至关重要。近年来，窄带成像（NBI）内镜已成为检测喉部病变的标准诊断工具，但其有效应用依赖经验丰富的临床医生，且耗时、易受观察者差异影响。在此背景下，人工智能（AI）为辅助临床决策提供了可行方案。本文提出将视觉变换器与注意力机制应用于窄带成像图像分析，以区分良性与恶性病变。结果显示，该方法分类性能良好，F1得分达82.72%，准确率为82.33%。此外，筛查结果具备可解释性：通过先进的分割方法MedSAM，定位出对临床有意义的病理区域信息，实现分类与分割融合，推动了喉癌筛查的智能化发展。
原文摘要 · Abstract (English)
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.
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