Faculty of Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada.
Digital Technologies Research Centre, National Research Council of Canada, Ottawa, ON K1A 0R6, Canada.
Sensors (Basel). 2023 Sep 27;23(19):8122. doi: 10.3390/s23198122.
Computer vision and deep learning have the potential to improve medical artificial intelligence (AI) by assisting in diagnosis, prediction, and prognosis. However, the application of deep learning to medical image analysis is challenging due to limited data availability and imbalanced data. While model performance is undoubtedly essential for medical image analysis, model trust is equally important. To address these challenges, we propose TRUDLMIA, a trustworthy deep learning framework for medical image analysis, which leverages image features learned through self-supervised learning and utilizes a novel surrogate loss function to build trustworthy models with optimal performance. The framework is validated on three benchmark data sets for detecting pneumonia, COVID-19, and melanoma, and the created models prove to be highly competitive, even outperforming those designed specifically for the tasks. Furthermore, we conduct ablation studies, cross-validation, and result visualization and demonstrate the contribution of proposed modules to both model performance (up to 21%) and model trust (up to 5%). We expect that the proposed framework will support researchers and clinicians in advancing the use of deep learning for dealing with public health crises, improving patient outcomes, increasing diagnostic accuracy, and enhancing the overall quality of healthcare delivery.
计算机视觉和深度学习有可能通过辅助诊断、预测和预后来改善医学人工智能 (AI)。然而，由于数据可用性有限和数据不平衡，深度学习在医学图像分析中的应用具有挑战性。虽然模型性能对于医学图像分析无疑是至关重要的，但模型可信度同样重要。为了解决这些挑战，我们提出了 TRUDLMIA，这是一个用于医学图像分析的可信深度学习框架，它利用通过自我监督学习学习到的图像特征，并利用新颖的替代损失函数来构建具有最佳性能的可信模型。该框架在三个用于检测肺炎、COVID-19 和黑色素瘤的基准数据集上进行了验证，所创建的模型被证明具有很强的竞争力，甚至超过了专门为这些任务设计的模型。此外，我们进行了消融研究、交叉验证和结果可视化，并证明了所提出的模块对模型性能（高达 21%）和模型可信度（高达 5%）的贡献。我们预计，所提出的框架将支持研究人员和临床医生推进深度学习在应对公共卫生危机、改善患者预后、提高诊断准确性和提高整体医疗保健服务质量方面的应用。
