  Despite the potential of weakly supervised learning to automatically annotate
massive amounts of data, little is known about its limitations for use in
computer-aided diagnosis (CAD). For CT specifically, interpreting the
performance of CAD algorithms can be challenging given the large number of
co-occurring diseases. This paper examines the effect of co-occurring diseases
when training classification models by weakly supervised learning, specifically
by comparing multi-label and multiple binary classifiers using the same
training data. Our results demonstrated that the binary model outperformed the
multi-label classification in every disease category in terms of AUC. However,
this performance was heavily influenced by co-occurring diseases in the binary
model, suggesting it did not always learn the correct appearance of the
specific disease. For example, binary classification of lung nodules resulted
in an AUC of < 0.65 when there were no other co-occurring diseases, but when
lung nodules co-occurred with emphysema, the performance reached AUC > 0.80. We
hope this paper revealed the complexity of interpreting disease classification
performance in weakly supervised models and will encourage researchers to
examine the effect of co-occurring diseases on classification performance in
the future.
