CheXseen: Unseen Disease Detection for Deep Learning Interpretation of Chest X-rays

We systematically evaluate the performance of deep learning models in the presence of diseases not labeled for or present during training. First, we evaluate whether deep learning models trained on a subset of diseases (seen diseases) can detect the presence of any one of a larger set of diseases. We find that models tend to falsely classify diseases outside of the subset (unseen diseases) as “no disease”. Second, we evaluate whether models trained on seen diseases can detect seen diseases when co-occurring with diseases outside the subset (unseen diseases). We find that models are still able to detect seen diseases even when co-occurring with unseen diseases. Third, we evaluate whether feature representations learned by models may be used to detect the presence of unseen diseases given a small labeled set of unseen diseases. We find that the penultimate layer of the deep neural network provides useful features for unseen disease detection. Our results can inform the safe clinical deployment of deep learning models trained on a non-exhaustive set of disease classes.

https://arxiv.org/abs/2103.04590

我们系统地评估了深度学习模型在未标注疾病上性能表现。首先,我们评估了深度学习模型在较小地数据集上预训练后在新的疾病种类上地测试表现。其次,我们评估了深度学习模型是否能判别已见过和未见过疾病的混合情况。我们发现在已见过和未见过病症同时出现的时候,深度学习模型依旧能检测到已见过的疾病。最后,我们评估了特征表示是否能检测到未见过的疾病在只有少量标签的情况下。我们发现深度学习模型的倒数第二层可以为未见过的疾病提供有用的特征。我们的结果展示了在不详尽的疾病种类上训练的深度学习模型部署是安全的。

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