  The diagnosis of the presence of metastatic lymph nodes from abdominal
computed tomography (CT) scans is an essential task performed by radiologists
to guide radiation and chemotherapy treatment. State-of-the-art deep learning
classifiers trained for this task usually rely on a training set containing CT
volumes and their respective image-level (i.e., global) annotation. However,
the lack of annotations for the localisation of the regions of interest (ROIs)
containing lymph nodes can limit classification accuracy due to the small size
of the relevant ROIs in this problem. The use of lymph node ROIs together with
global annotations in a multi-task training process has the potential to
improve classification accuracy, but the high cost involved in obtaining the
ROI annotation for the same samples that have global annotations is a roadblock
for this alternative. We address this limitation by introducing a new training
strategy from two data sets: one containing the global annotations, and another
(publicly available) containing only the lymph node ROI localisation. We term
our new strategy semi-supervised multi-domain multi-task training, where the
goal is to improve the diagnosis accuracy on the globally annotated data set by
incorporating the ROI annotations from a different domain. Using a private data
set containing global annotations and a public data set containing lymph node
ROI localisation, we show that our proposed training mechanism improves the
area under the ROC curve for the classification task compared to several
training method baselines.
