论文标题
DeepTechnome：减轻基于深度学习的CT图像评估中未知偏差
DeepTechnome: Mitigating Unknown Bias in Deep Learning Based Assessment of CT Images
论文作者
论文摘要
使用相关的生物学信息可靠地检测疾病对于在医学成像中深度学习技术的现实适用性至关重要。我们在针对未知偏见的训练过程中进行DEBIAS深度学习模型 - 无需事先预处理/过滤输入或假设有关其分布或数据集中的精确性的特定知识。我们将控制区域用作携带有关偏见的信息，采用分类器模型提取功能的替代区域，并使用我们的自定义模块化Decorrelayer抑制偏见的中间功能。我们通过引入模拟偏见W.R.T.在952肺计算机断层扫描的数据集上评估我们的方法。重建内核和噪声水平，并提出在评估偏置技术评估中包括对抗性测试集。在适度尺寸的模型架构中，应用了提出的方法从表现出强偏见的数据中学习，它几乎可以完美地恢复使用相应无偏见的训练时观察到的分类性能。
Reliably detecting diseases using relevant biological information is crucial for real-world applicability of deep learning techniques in medical imaging. We debias deep learning models during training against unknown bias - without preprocessing/filtering the input beforehand or assuming specific knowledge about its distribution or precise nature in the dataset. We use control regions as surrogates that carry information regarding the bias, employ the classifier model to extract features, and suppress biased intermediate features with our custom, modular DecorreLayer. We evaluate our method on a dataset of 952 lung computed tomography scans by introducing simulated biases w.r.t. reconstruction kernel and noise level and propose including an adversarial test set in evaluations of bias reduction techniques. In a moderately sized model architecture, applying the proposed method to learn from data exhibiting a strong bias, it near-perfectly recovers the classification performance observed when training with corresponding unbiased data.
