PDF(Pubmed)
Abstract：
Colorectal cancer is one of the top contributors to cancer-related deaths in the United States, with over 100,000 estimated cases in 2020 and over 50,000 deaths. The most common screening technique is minimally invasive colonoscopy using either reflected white light endoscopy or narrow-band imaging. However, current imaging modalities have only moderate sensitivity and specificity for lesion detection. We have developed a novel fluorescence excitation-scanning hyperspectral imaging (HSI) approach to sample image and spectroscopic data simultaneously on microscope and endoscope platforms for enhanced diagnostic potential. Unfortunately, fluorescence excitation-scanning HSI datasets pose major challenges for data processing, interpretability, and classification due to their high dimensionality. Here, we present an end-to-end scalable Artificial Intelligence (AI) framework built for classification of excitation-scanning HSI microscopy data that provides accurate image classification and interpretability of the AI decision-making process. The developed AI framework is able to perform real-time HSI classification with different speed/classification performance trade-offs by tailoring the dimensionality of the dataset, supporting different dimensions of deep learning models, and varying the architecture of deep learning models. We have also incorporated tools to visualize the exact location of the lesion detected by the AI decision-making process and to provide heatmap-based pixel-by-pixel interpretability. In addition, our deep learning framework provides wavelength-dependent impact as a heatmap, which allows visualization of the contributions of HSI wavelength bands during the AI decision-making process. This framework is well-suited for HSI microscope and endoscope platforms, where real-time analysis and visualization of classification results are required by clinicians.
摘要：
结直肠癌是美国癌症相关死亡的主要原因之一，2020年估计有超过100,000例病例，超过50,000例死亡。最常见的筛查技术是使用反射白光内窥镜或窄带成像的微创结肠镜检查。然而,目前的成像模式对病变检测只有中等的敏感性和特异性.我们开发了一种新颖的荧光激发扫描高光谱成像（HSI）方法，可在显微镜和内窥镜平台上同时获得样品图像和光谱数据，以增强诊断潜力。不幸的是,荧光激发扫描HSI数据集对数据处理提出了重大挑战，可解释性,和分类，因为它们的高维性。这里,我们提出了一个端到端可扩展的人工智能（AI）框架，用于对激励扫描HSI显微镜数据进行分类，该框架可提供准确的图像分类和AI决策过程的可解释性。开发的AI框架能够通过定制数据集的维度，以不同的速度/分类性能权衡来执行实时HSI分类，支持不同维度的深度学习模型，并改变深度学习模型的架构。我们还结合了工具来可视化AI决策过程检测到的病变的确切位置，并提供基于热图的逐像素可解释性。此外,我们的深度学习框架提供了依赖于波长的影响作为热图，它允许在AI决策过程中可视化HSI波段的贡献。该框架非常适合HSI显微镜和内窥镜平台，临床医生需要实时分析和可视化分类结果。
