There is an increasing number of medical use-cases where classification algorithms based on deep neural networks reach performance levels that are competitive with human medical experts. To alleviate the challenges of small dataset sizes, these systems often rely on pretraining. In this work, we aim to assess the broader implications of these approaches. For diabetic retinopathy grading as exemplary use case, we compare the impact of different training procedures including recently established self-supervised pretraining methods based on contrastive learning. To this end, we investigate different aspects such as quantitative performance, statistics of the learned feature representations, interpretability and robustness to image distortions. Our results indicate that models initialized from ImageNet pretraining report a significant increase in performance, generalization and robustness to image distortions. In particular, self-supervised models show further benefits to supervised models. Self-supervised models with initialization from ImageNet pretraining not only report higher performance, they also reduce overfitting to large lesions along with improvements in taking into account minute lesions indicative of the progression of the disease. Understanding the effects of pretraining in a broader sense that goes beyond simple performance comparisons is of crucial importance for the broader medical imaging community beyond the use-case considered in this work.
翻译:根据深神经网络制定的分类算法达到与人类医学专家具有竞争力的性能水平的分类算法,这种算法与人类医学专家具有竞争力。为减轻小数据集规模的挑战,这些系统往往依靠预培训。在这项工作中,我们的目标是评估这些方法的更广泛影响。对于糖尿病视网病病的评级作为示范性使用案例,我们比较了不同培训程序的影响,包括最近根据对比性学习而建立的自我监督的训练前方法。为此,我们调查了不同方面,例如数量性能、所学特征表现的统计、可解释性和对图像扭曲的稳健性。我们的结果表明,从图像网预培训开始的模型报告性能、一般化和稳健性对图像扭曲的显著提高。特别是,自我监督的模型显示了监督模型的进一步好处。从图像网初始化的模型不仅报告较高的性能,而且还减少了过度适应大的损害,同时考虑到病情演变的微小损害的改善。我们从更广泛的意义上理解培训前培训的影响,这超出了简单的业绩比较,对于更广泛的医学成像工作至关重要。