Département d'informatique et de recherche opérationnelle, Université de Montréal, 2920 chemin de la Tour, Montréal, H3T 1J4, QC, Canada.
Mila - Quebec AI Institute, 6666 Rue Saint-Urbain, Montréal, H2S 3H1, QC, Canada.
BMC Med Inform Decis Mak. 2024 May 16;24(1):126. doi: 10.1186/s12911-024-02529-9.
Chest X-ray imaging based abnormality localization, essential in diagnosing various diseases, faces significant clinical challenges due to complex interpretations and the growing workload of radiologists. While recent advances in deep learning offer promising solutions, there is still a critical issue of domain inconsistency in cross-domain transfer learning, which hampers the efficiency and accuracy of diagnostic processes. This study aims to address the domain inconsistency problem and improve autonomic abnormality localization performance of heterogeneous chest X-ray image analysis, particularly in detecting abnormalities, by developing a self-supervised learning strategy called "BarlwoTwins-CXR".
We utilized two publicly available datasets: the NIH Chest X-ray Dataset and the VinDr-CXR. The BarlowTwins-CXR approach was conducted in a two-stage training process. Initially, self-supervised pre-training was performed using an adjusted Barlow Twins algorithm on the NIH dataset with a Resnet50 backbone pre-trained on ImageNet. This was followed by supervised fine-tuning on the VinDr-CXR dataset using Faster R-CNN with Feature Pyramid Network (FPN). The study employed mean Average Precision (mAP) at an Intersection over Union (IoU) of 50% and Area Under the Curve (AUC) for performance evaluation.
Our experiments showed a significant improvement in model performance with BarlowTwins-CXR. The approach achieved a 3% increase in mAP50 accuracy compared to traditional ImageNet pre-trained models. In addition, the Ablation CAM method revealed enhanced precision in localizing chest abnormalities. The study involved 112,120 images from the NIH dataset and 18,000 images from the VinDr-CXR dataset, indicating robust training and testing samples.
BarlowTwins-CXR significantly enhances the efficiency and accuracy of chest X-ray image-based abnormality localization, outperforming traditional transfer learning methods and effectively overcoming domain inconsistency in cross-domain scenarios. Our experiment results demonstrate the potential of using self-supervised learning to improve the generalizability of models in medical settings with limited amounts of heterogeneous data. This approach can be instrumental in aiding radiologists, particularly in high-workload environments, offering a promising direction for future AI-driven healthcare solutions.
基于胸部 X 光成像的异常定位在诊断各种疾病中至关重要，但由于复杂的解释和放射科医生工作量的增加，这一过程面临着重大的临床挑战。虽然深度学习的最新进展提供了有希望的解决方案，但在跨域迁移学习中仍然存在一个关键的领域不一致问题，这阻碍了诊断过程的效率和准确性。本研究旨在通过开发一种名为“BarlwoTwins-CXR”的自监督学习策略来解决领域不一致问题，并提高异构胸部 X 光图像分析的自主异常定位性能，特别是在检测异常方面。
我们使用了两个公开数据集：NIH 胸部 X 光数据集和 VinDr-CXR。BarlwoTwins-CXR 方法在两阶段训练过程中进行。首先，使用调整后的 Barlow Twins 算法在 NIH 数据集上进行自监督预训练，该算法使用在 ImageNet 上预训练的 Resnet50 骨干。然后，在 VinDr-CXR 数据集上使用带有特征金字塔网络（FPN）的 Faster R-CNN 进行有监督的微调。研究采用平均精度（mAP）和曲线下面积（AUC）在交并比（IoU）为 50%的情况下进行性能评估。
我们的实验表明，BarlwoTwins-CXR 显著提高了模型性能。该方法在 mAP50 准确性方面比传统的 ImageNet 预训练模型提高了 3%。此外，Ablation CAM 方法显示出在定位胸部异常方面的精度提高。研究涉及来自 NIH 数据集的 112120 张图像和来自 VinDr-CXR 数据集的 18000 张图像，表明训练和测试样本具有很强的稳健性。
BarlwoTwins-CXR 显著提高了基于胸部 X 光图像的异常定位的效率和准确性，优于传统的迁移学习方法，并有效地克服了跨域场景中的领域不一致问题。我们的实验结果表明，使用自监督学习来提高模型在医疗环境中的泛化能力是可行的，即使在数据量有限的情况下也是如此。这种方法可以为放射科医生提供帮助，特别是在高工作量环境下，为未来的人工智能驱动的医疗保健解决方案提供了有前途的方向。
