Zhang Lei, Wu Linjie, Wei Liangzhuang, Wu Haitao, Lin Yandan
Academy for Engineering and Technology, Fudan University, Handan 220, Shanghai 200433, China.
ENT Institute and Otorhinolaryngology Department, Eye & ENT Hospital of Fudan University, Shanghai 200433, China.
Diagnostics (Basel). 2023 Mar 17;13(6):1151. doi: 10.3390/diagnostics13061151.
Narrow band imaging is an established non-invasive tool used for the early detection of laryngeal cancer in surveillance examinations. Most images produced from the examination are useless, such as blurred, specular reflection, and underexposed. Removing the uninformative frames is vital to improve detection accuracy and speed up computer-aided diagnosis. It often takes a lot of time for the physician to manually inspect the informative frames. This issue is commonly addressed by a classifier with task-specific categories of the uninformative frames. However, the definition of the uninformative categories is ambiguous, and tedious labeling still cannot be avoided. Here, we show that a novel unsupervised scheme is comparable to the current benchmarks on the dataset of NBI-InfFrames. We extract feature embedding using a vanilla neural network (VGG16) and introduce a new dimensionality reduction method called UMAP that distinguishes the feature embedding in the lower-dimensional space. Along with the proposed automatic cluster labeling algorithm and cost function in Bayesian optimization, the proposed method coupled with UMAP achieves state-of-the-art performance. It outperforms the baseline by 12% absolute. The overall median recall of the proposed method is currently the highest, 96%. Our results demonstrate the effectiveness of the proposed scheme and the robustness of detecting the informative frames. It also suggests the patterns embedded in the data help develop flexible algorithms that do not require manual labeling.
窄带成像技术是一种成熟的非侵入性工具，用于在监测检查中早期检测喉癌。检查产生的大多数图像都是无用的，例如模糊、镜面反射和曝光不足。去除无信息的帧对于提高检测准确性和加快计算机辅助诊断至关重要。医生手动检查有信息的帧通常需要花费大量时间。这个问题通常通过具有针对无信息帧的特定任务类别的分类器来解决。然而，无信息类别的定义是模糊的，并且仍然无法避免繁琐的标记。在这里，我们表明一种新颖的无监督方案在NBI-InfFrames数据集上与当前基准相当。我们使用普通神经网络（VGG16）提取特征嵌入，并引入一种称为UMAP的新降维方法，该方法在低维空间中区分特征嵌入。结合提出的自动聚类标记算法和贝叶斯优化中的成本函数，与UMAP相结合的提出方法实现了最先进的性能。它比基线绝对高出12%。所提出方法的总体中位数召回率目前是最高的，为96%。我们的结果证明了所提出方案的有效性以及检测有信息帧的稳健性。它还表明数据中嵌入的模式有助于开发不需要手动标记的灵活算法。
