University of Applied Sciences Western Switzerland (HES-SO), 3960, Sierre, Switzerland.
Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, AGH University of Krakow, 30059, Kraków, Poland.
Sci Rep. 2023 Nov 9;13(1):19518. doi: 10.1038/s41598-023-46345-z.
The analysis of veterinary radiographic imaging data is an essential step in the diagnosis of many thoracic lesions. Given the limited time that physicians can devote to a single patient, it would be valuable to implement an automated system to help clinicians make faster but still accurate diagnoses. Currently, most of such systems are based on supervised deep learning approaches. However, the problem with these solutions is that they need a large database of labeled data. Access to such data is often limited, as it requires a great investment of both time and money. Therefore, in this work we present a solution that allows higher classification scores to be obtained using knowledge transfer from inter-species and inter-pathology self-supervised learning methods. Before training the network for classification, pretraining of the model was performed using self-supervised learning approaches on publicly available unlabeled radiographic data of human and dog images, which allowed substantially increasing the number of images for this phase. The self-supervised learning approaches included the Beta Variational Autoencoder, the Soft-Introspective Variational Autoencoder, and a Simple Framework for Contrastive Learning of Visual Representations. After the initial pretraining, fine-tuning was performed for the collected veterinary dataset using 20% of the available data. Next, a latent space exploration was performed for each model after which the encoding part of the model was fine-tuned again, this time in a supervised manner for classification. Simple Framework for Contrastive Learning of Visual Representations proved to be the most beneficial pretraining method. Therefore, it was for this method that experiments with various fine-tuning methods were carried out. We achieved a mean ROC AUC score of 0.77 and 0.66, respectively, for the laterolateral and dorsoventral projection datasets. The results show significant improvement compared to using the model without any pretraining approach.
兽医放射影像学数据的分析是诊断许多胸部病变的重要步骤。由于医生给单个患者的时间有限，因此实施一个自动化系统来帮助临床医生更快地做出但仍然准确的诊断是很有价值的。目前，大多数此类系统都是基于有监督的深度学习方法。然而，这些解决方案的问题在于它们需要大量标记数据的数据库。访问此类数据通常受到限制，因为它需要大量的时间和资金投入。因此，在这项工作中，我们提出了一种解决方案，该解决方案允许通过物种间和跨病理学的自我监督学习方法的知识转移来获得更高的分类分数。在进行网络分类训练之前，使用自我监督学习方法对公开的人类和狗图像的未标记放射图像进行了模型的预训练，这使得此阶段的图像数量大大增加。自我监督学习方法包括 Beta 变分自动编码器、Soft-Introspective 变分自动编码器和简单的视觉表示对比学习框架。在初始预训练之后，使用可用数据的 20%对收集的兽医数据集进行微调。接下来，对每个模型进行潜在空间探索，然后再次以监督方式对模型的编码部分进行微调，这次是用于分类。简单的视觉表示对比学习框架被证明是最有益的预训练方法。因此，对于这种方法，进行了各种微调方法的实验。我们分别为侧位和背腹位投影数据集实现了 0.77 和 0.66 的平均 ROC AUC 分数。与没有任何预训练方法的模型相比，结果显示出了显著的改进。
