  Echocardiography (echo) is the first imaging modality used when assessing
cardiac function. The measurement of functional biomarkers from echo relies
upon the segmentation of cardiac structures and deep learning models have been
proposed to automate the segmentation process. However, in order to translate
these tools to widespread clinical use it is important that the segmentation
models are robust to a wide variety of images (e.g. acquired from different
scanners, by operators with different levels of expertise etc.). To achieve
this level of robustness it is necessary that the models are trained with
multiple diverse datasets. A significant challenge faced when training with
multiple diverse datasets is the variation in label presence, i.e. the combined
data are often partially-labelled. Adaptations of the cross entropy loss
function have been proposed to deal with partially labelled data. In this paper
we show that training naively with such a loss function and multiple diverse
datasets can lead to a form of shortcut learning, where the model associates
label presence with domain characteristics, leading to a drop in performance.
To address this problem, we propose a novel label dropout scheme to break the
link between domain characteristics and the presence or absence of labels. We
demonstrate that label dropout improves echo segmentation Dice score by 62% and
25% on two cardiac structures when training using multiple diverse partially
labelled datasets.
