Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, China.
Department of Spinal Surgery, The Fifth Affiliated Hospital of Sun Yat-sen University, China.
Comput Biol Med. 2024 Aug;178:108754. doi: 10.1016/j.compbiomed.2024.108754. Epub 2024 Jun 15.
Lumbar disc herniation (LDH) is a prevalent spinal disease that can result in severe pain, with Magnetic resonance imaging (MRI) serving as a commonly diagnostic tool. However, annotating numerous MRI images, necessary for deep learning based LDH diagnosis, can be challenging and labor-intensive. Semi-supervised learning, mainly utilizing pseudo labeling and consistency regularization, can leverage limited labeled images and abundant unlabeled images. However, consistency regularization solely focuses on maintaining the semantic consistency of transformed unlabeled data but fails to utilize the semantic information from labeled data to guide the unlabeled data, and additionally, pseudo labeling is prone to confirmation bias.
We propose SeCoFixMatch, an innovative approach that seamlessly integrates semantic contrast and uncertainty-aware pseudo labeling into semi-supervised learning. Semantic contrast constraints the semantic consistency between labeled and unlabeled images. Pseudo labels are generated by combining predictive confidence and uncertainty, with uncertainty computing by optimizing the Kullback-Leibler (KL) loss between predictive and target Dirichlet distribution.
Comparison with other semi-supervised models and ablation experiment with varying labeled data demonstrate the effectiveness and generalization of proposed model. Notably, SeCoFixMatch, trained with just 40 labels, outperforms the baseline model trained with 200 labels, reducing the annotation effort by a remarkable 80%.
Proposed pseudo labeling algorithm generates more precise pseudo labels for semantic contrastive learning and semantic contrastive learning facilitates better feature representation, thereby further improving the prediction accuracy of pseudo label. The mutual reinforcement of pseudo labeling and semantic contrast constraints boosts the performance of semi-supervised algorithm.
腰椎间盘突出症（LDH）是一种常见的脊柱疾病，可导致严重疼痛，磁共振成像（MRI）是常用的诊断工具。然而，为了基于深度学习的 LDH 诊断，需要对大量的 MRI 图像进行注释，这可能具有挑战性和劳动密集性。半监督学习主要利用伪标记和一致性正则化，可以利用有限的标记图像和大量的未标记图像。然而，一致性正则化仅关注于保持变换后未标记数据的语义一致性，但未能利用标记数据的语义信息来指导未标记数据，并且伪标记容易受到确认偏差的影响。
我们提出了 SeCoFixMatch，这是一种将语义对比和不确定性感知伪标记无缝集成到半监督学习中的创新方法。语义对比约束了标记和未标记图像之间的语义一致性。伪标记是通过结合预测置信度和不确定性生成的，不确定性是通过优化预测和目标 Dirichlet 分布之间的 Kullback-Leibler（KL）损失来计算的。
与其他半监督模型的比较和具有不同标记数据的消融实验证明了所提出模型的有效性和泛化能力。值得注意的是，仅用 40 个标签训练的 SeCoFixMatch 优于用 200 个标签训练的基线模型，注释工作量减少了 80%。
所提出的伪标记算法为语义对比学习生成了更精确的伪标记，而语义对比学习促进了更好的特征表示，从而进一步提高了伪标记的预测准确性。伪标记和语义对比约束的相互增强提高了半监督算法的性能。
