■肺癌是一种恶性肿瘤,肺结节被认为是重要指标。早期识别并及时治疗肺结节有助于提高肿瘤患者的生存率。正电子发射断层扫描-计算机断层扫描(PET/CT)是一种非侵入性,融合成像技术可以同时获得肺部区域的功能和结构信息。然而,由于依赖于结节的注释,基于计算机辅助诊断的肺结节研究主要集中在结节水平,这是肤浅的,无法有助于实际的临床诊断。因此,这项研究的目的是开发一个全自动分类框架,以更全面地评估PET/CT成像数据中的肺结节。
■我们开发了一个两阶段多模态学习框架,用于在PET/CT成像中诊断肺结节。在这个框架中,第一阶段侧重于使用预训练的U-Net和PET/CT配准进行肺实质分割。第二阶段旨在提取,集成,并通过采用三维(3D)Inception-残差网(ResNet)卷积块注意力模块架构和密集投票融合机制来识别图像级和特征级特征。
■在实验中,使用一组真实的临床数据全面验证了所提出的模型的性能,平均得分为89.98%,89.21%,84.75%,93.38%,86.83%,和0.9227的准确性,精度,召回,特异性,F1得分,和曲线下面积值,分别。
■本文提出了一种用于肺结节自动诊断的两阶段多模态学习方法。研究结果表明,肺结节诊断中结节的非孤立性是模型性能受限的主要原因,为今后的研究提供方向。
UNASSIGNED: Lung cancer is a malignant tumor, for which pulmonary nodules are considered to be significant indicators. Early recognition and timely treatment of pulmonary nodules can contribute to improving the survival rate of patients with cancer. Positron emission tomography-computed tomography (PET/CT) is a noninvasive, fusion imaging technique that can obtain both functional and structural information of lung regions. However, studies of pulmonary nodules based on computer-aided diagnosis have primarily focused on the nodule level due to a reliance on the annotation of nodules, which is superficial and unable to contribute to the actual clinical diagnosis. The aim of this study was thus to develop a fully automated classification framework for a more comprehensive assessment of pulmonary nodules in PET/CT imaging data.
UNASSIGNED: We developed a two-stage
multimodal learning framework for the diagnosis of pulmonary nodules in PET/CT imaging. In this framework, Stage I focuses on pulmonary parenchyma segmentation using a pretrained U-Net and PET/CT registration. Stage II aims to extract, integrate, and recognize image-level and feature-level features by employing the three-dimensional (3D) Inception-residual net (ResNet) convolutional block attention module architecture and a dense-voting fusion mechanism.
UNASSIGNED: In the experiments, the proposed model\'s performance was comprehensively validated using a set of real clinical data, achieving mean scores of 89.98%, 89.21%, 84.75%, 93.38%, 86.83%, and 0.9227 for accuracy, precision, recall, specificity, F1 score, and area under curve values, respectively.
UNASSIGNED: This paper presents a two-stage
multimodal learning approach for the automatic diagnosis of pulmonary nodules. The findings reveal that the main reason for limiting model performance is the nonsolitary property of nodules in pulmonary nodule diagnosis, providing direction for future research.