School of Mathematics and Statistics, Lanzhou University, Lanzhou, China.
Gansu Provincial Key Laboratory of Wearable Computing, Lanzhou University, Lanzhou, China.
Med Phys. 2024 Aug;51(8):5550-5562. doi: 10.1002/mp.17130. Epub 2024 May 16.
Liver fibrosis poses a significant public health challenge given its elevated incidence and associated mortality rates. Diffusion-Weighted Imaging (DWI) serves as a non-invasive diagnostic tool for supporting the identification of liver fibrosis. Deep learning, as a computer-aided diagnostic technology, can assist in recognizing the stage of liver fibrosis by extracting abstract features from DWI images. However, gathering samples is often challenging, posing a common dilemma in previous research. Moreover, previous studies frequently overlooked the cross-comparison information and latent connections among different DWI parameters. Thus, it is becoming a challenge to identify effective DWI parameters and dig potential features from multiple categories in a dataset with limited samples.
A self-defined Multi-view Contrastive Learning Network is developed to automatically classify multi-parameter DWI images and explore synergies between different DWI parameters.
A Dense-fusion Attention Contrastive Learning Network (DACLN) is designed and used to recognize DWI images. Concretely, a multi-view contrastive learning framework is constructed to train and extract features from raw multi-parameter DWI. Besides, a Dense-fusion module is designed to integrate feature and output predicted labels.
We evaluated the performance of the proposed model on a set of real clinical data and analyzed the interpretability by Grad-CAM and annotation analysis, achieving average scores of 0.8825, 0.8702, 0.8933, 0.8727, and 0.8779 for accuracy, precision, recall, specificity and F-1 score. Of note, the experimental results revealed that IVIM-f, CTRW-β, and MONO-ADC exhibited significant recognition ability and complementarity.
Our method achieves competitive accuracy in liver fibrosis diagnosis using the limited multi-parameter DWI dataset and finds three types of DWI parameters with high sensitivity for diagnosing liver fibrosis, which suggests potential directions for future research.
肝纤维化的发病率和相关死亡率都很高，因此它是一个重大的公共卫生挑战。弥散加权成像（DWI）是一种支持识别肝纤维化的非侵入性诊断工具。深度学习作为一种计算机辅助诊断技术，可以通过从 DWI 图像中提取抽象特征来帮助识别肝纤维化的阶段。然而，样本采集通常具有挑战性，这是以前研究中的一个常见难题。此外，以前的研究经常忽略不同 DWI 参数之间的交叉比较信息和潜在联系。因此，在一个样本量有限的数据集，从多个类别中识别有效 DWI 参数并挖掘潜在特征成为一个挑战。
开发了一个自定义的多视图对比学习网络，以自动分类多参数 DWI 图像并探索不同 DWI 参数之间的协同作用。
设计并使用密集融合注意力对比学习网络（DACLN）来识别 DWI 图像。具体来说，构建了一个多视图对比学习框架，从原始多参数 DWI 中训练和提取特征。此外，设计了一个密集融合模块来整合特征并输出预测标签。
我们在一组真实的临床数据上评估了所提出模型的性能，并通过 Grad-CAM 和注释分析来分析可解释性，得到了 0.8825、0.8702、0.8933、0.8727 和 0.8779 的平均准确率、精确率、召回率、特异性和 F1 分数。值得注意的是，实验结果表明 IVIM-f、CTRW-β 和 MONO-ADC 具有显著的识别能力和互补性。
我们的方法在使用有限的多参数 DWI 数据集进行肝纤维化诊断时达到了有竞争力的准确率，并发现了三种对诊断肝纤维化具有高灵敏度的 DWI 参数，这为未来的研究提供了潜在的方向。
