自学式半监督上肢X线异常检测
Self-Taught Semi-Supervised Anomaly Detection on Upper Limb X-rays
摘要：
检测肌肉骨骼X光片中的异常对于放射学工作流程中的大规模筛查至关重要。受监督的深度网络认为放射科医生的大量注释是理所当然的，这通常是非常耗时的。此外，受监督的系统被定制为闭集场景，例如，训练的模型在训练时会过度拟合到先前看到的罕见异常。相反，我们的方法的基本原理是使用任务不可知的借口任务，利用跨样本相似性度量的基础上的未标记的数据。此外，我们制定了一个复杂的分布数据从正常类在我们的框架内，以避免潜在的偏见的一方异常。通过大量的实验，我们证明了我们的方法在真实世界的医疗数据集MURA数据集上的无监督和自监督异常检测设置中优于基线。我们还提供了丰富的消融研究，以分析每个训练阶段的效果和最终性能的损失条款。
英文摘要：
Detecting anomalies in musculoskeletal radiographs is of paramount importance for large-scale screening in the radiology workflow. Supervised deep networks take for granted a large number of annotations by radiologists, which is often prohibitively very time-consuming to acquire. Moreover, supervised systems are tailored to closed set scenarios, e.g., trained models suffer from overfitting to previously seen rare anomalies at training. Instead, our approach's rationale is to use task agnostic pretext tasks to leverage unlabeled data based on a cross-sample similarity measure. Besides, we formulate a complex distribution of data from normal class within our framework to avoid a potential bias on the side of anomalies. Through extensive experiments, we show that our method outperforms baselines across unsupervised and self-supervised anomaly detection settings on a real-world medical dataset, the MURA dataset. We also provide rich ablation studies to analyze each training stage's effect and loss terms on the final performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
