Neural networks can learn spurious correlations that lead to the correct
prediction in a validation set, but generalise poorly because the predictions
are right for the wrong reason. This undesired learning of naive shortcuts
(Clever Hans effect) can happen for example in echocardiogram view
classification when background cues (e.g. metadata) are biased towards a class
and the model learns to focus on those background features instead of on the
image content. We propose a simple, yet effective random background
augmentation method called BackMix, which samples random backgrounds from other
examples in the training set. By enforcing the background to be uncorrelated
with the outcome, the model learns to focus on the data within the ultrasound
sector and becomes invariant to the regions outside this. We extend our method
in a semi-supervised setting, finding that the positive effects of BackMix are
maintained with as few as 5% of segmentation labels. A loss weighting
mechanism, wBackMix, is also proposed to increase the contribution of the
augmented examples. We validate our method on both in-distribution and
out-of-distribution datasets, demonstrating significant improvements in
classification accuracy, region focus and generalisability. Our source code is
available at: this https URL

神经网络可以学习到正确预测验证集的偶然相关性，但因为预测的原因错误而泛化能力较差。我们提出了一种简单但有效的称为 BackMix 的随机背景增强方法，该方法从其他训练集样本中随机选择背景。通过强制使背景与结果不相关，模型学习将重点放在超声扇区的数据上，并对其它区域保持不变性。我们在半监督学习环境中扩展了我们的方法，并发现 BackMix 的正面效果即使在只有 5% 的分割标签的情况下也能保持。还提出了一种损失加权机制 wBackMix，以增加增强样本的贡献。我们在分布内和分布外数据集上验证了我们的方法，在分类准确性、区域关注度和泛化能力方面取得了显著改进。我们的源代码可在此 https URL 获取。