标题： 高效准确的结核病诊断：基于注意力残差U-Net和视觉变压器的检测框架 显示英文标题

标题： Efficient and Accurate Tuberculosis Diagnosis: Attention Residual U-Net and Vision Transformer Based Detection Framework

摘要： 结核病（TB）是由结核分枝杆菌引起的传染病，尽管可以预防和治愈，但仍然是全球主要的健康威胁。这种负担在低收入和中等收入国家尤其严重。显微镜检查仍然是通过直接观察痰涂片样本中的结核分枝杆菌来诊断TB的关键方法，为早期检测和有效治疗提供了成本效益高的方法。鉴于显微镜检查的工作量大，自动化显微图像中杆菌的检测对于提高TB诊断的速度和可靠性至关重要。目前在明场显微镜痰涂片图像中检测结核杆菌的方法受到有限的自动化能力、不一致的分割质量和受限的分类精度的阻碍。本文提出了一种两阶段的深度学习方法用于结核杆菌检测，包括杆菌分割和分类。在初始阶段，提出了一种采用注意力块和残差连接的先进U-Net模型来分割显微镜痰涂片图像，从而提取感兴趣区域（ROIs）。然后使用定制的Vision Transformer对提取的ROIs进行分类，我们特别将其定制为TBViT以提高图像中杆菌的精确检测。在实验中，使用了一个新开发的显微镜痰涂片图像数据集，该数据集来源于齐尔-尼尔森染色的切片，并与现有的公开数据集结合使用。使用各种指标对实验进行定性和定量评估表明，所提出的模型在分割性能、分类准确率和自动化程度方面均显著优于现有方法。 显示英文摘要

摘要： Tuberculosis (TB), an infectious disease caused by Mycobacterium tuberculosis, continues to be a major global health threat despite being preventable and curable. This burden is particularly high in low and middle income countries. Microscopy remains essential for diagnosing TB by enabling direct visualization of Mycobacterium tuberculosis in sputum smear samples, offering a cost effective approach for early detection and effective treatment. Given the labour-intensive nature of microscopy, automating the detection of bacilli in microscopic images is crucial to improve both the expediency and reliability of TB diagnosis. The current methodologies for detecting tuberculosis bacilli in bright field microscopic sputum smear images are hindered by limited automation capabilities, inconsistent segmentation quality, and constrained classification precision. This paper proposes a twostage deep learning methodology for tuberculosis bacilli detection, comprising bacilli segmentation followed by classification. In the initial phase, an advanced U-Net model employing attention blocks and residual connections is proposed to segment microscopic sputum smear images, enabling the extraction of Regions of Interest (ROIs). The extracted ROIs are then classified using a Vision Transformer, which we specifically customized as TBViT to enhance the precise detection of bacilli within the images. For the experiments, a newly developed dataset of microscopic sputum smear images derived from Ziehl-Neelsen-stained slides is used in conjunction with existing public datasets. The qualitative and quantitative evaluation of the experiments using various metrics demonstrates that the proposed model achieves significantly improved segmentation performance, higher classification accuracy, and a greater level of automation, surpassing existing methods.