  In clinical practice, medical image interpretation often involves
multi-labeled classification, since the affected parts of a patient tend to
present multiple symptoms or comorbidities. Recently, deep learning based
frameworks have attained expert-level performance on medical image
interpretation, which can be attributed partially to large amounts of accurate
annotations. However, manually annotating massive amounts of medical images is
impractical, while automatic annotation is fast but imprecise (possibly
introducing corrupted labels). In this work, we propose a new regularization
approach, called Flow-Mixup, for multi-labeled medical image classification
with corrupted labels. Flow-Mixup guides the models to capture robust features
for each abnormality, thus helping handle corrupted labels effectively and
making it possible to apply automatic annotation. Specifically, Flow-Mixup
decouples the extracted features by adding constraints to the hidden states of
the models. Also, Flow-Mixup is more stable and effective comparing to other
known regularization methods, as shown by theoretical and empirical analyses.
Experiments on two electrocardiogram datasets and a chest X-ray dataset
containing corrupted labels verify that Flow-Mixup is effective and insensitive
to corrupted labels.
