  Due to the high human cost of annotation, it is non-trivial to curate a
large-scale medical dataset that is fully labeled for all classes of interest.
Instead, it would be convenient to collect multiple small partially labeled
datasets from different matching sources, where the medical images may have
only been annotated for a subset of classes of interest. This paper offers an
empirical understanding of an under-explored problem, namely partially
supervised multi-label classification (PSMLC), where a multi-label classifier
is trained with only partially labeled medical images. In contrast to the fully
supervised counterpart, the partial supervision caused by medical data scarcity
has non-trivial negative impacts on the model performance. A potential remedy
could be augmenting the partial labels. Though vicinal risk minimization (VRM)
has been a promising solution to improve the generalization ability of the
model, its application to PSMLC remains an open question. To bridge the
methodological gap, we provide the first VRM-based solution to PSMLC. The
empirical results also provide insights into future research directions on
partially supervised learning under data scarcity.
